{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training a second model\n",
    "\n",
    "In this notebook, I train a second model using features in order to address the first model's shortcomings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package vader_lexicon to\n",
      "[nltk_data]     /Users/emmanuel.ameisen/nltk_data...\n",
      "[nltk_data]   Package vader_lexicon is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from pathlib import Path\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, classification_report\n",
    "from sklearn.externals import joblib\n",
    "import sys\n",
    "sys.path.append(\"..\")\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from ml_editor.data_processing import (\n",
    "    format_raw_df,\n",
    "    get_split_by_author,\n",
    "    train_vectorizer, \n",
    "    get_vectorized_series,\n",
    "    get_feature_vector_and_label\n",
    ")\n",
    "from ml_editor.model_evaluation import (\n",
    "    get_feature_importance,\n",
    "    get_roc_plot,\n",
    "    get_confusion_matrix_plot,\n",
    "    get_calibration_plot\n",
    ")\n",
    "\n",
    "from ml_editor.model_v2 import (\n",
    "    add_char_count_features, \n",
    "    get_word_stats, \n",
    "    get_sentiment_score, \n",
    "    POS_NAMES,\n",
    "    get_question_score_from_input\n",
    ")\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "np.random.seed(35)\n",
    "\n",
    "data_path = Path('../data/writers.csv')\n",
    "df = pd.read_csv(data_path)\n",
    "df = format_raw_df(df.copy())\n",
    "df = df.loc[df[\"is_question\"]].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"full_text\"] = df[\"Title\"].str.cat(df[\"body_text\"], sep=\" \", na_rep=\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's add new features we've identified as potential candidates in our new model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df, test_df = get_split_by_author(df, test_size=0.2, random_state=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorizer = train_vectorizer(train_df)\n",
    "df[\"vectors\"] = get_vectorized_series(df[\"full_text\"].copy(), vectorizer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check out the ml_editor source code to see more about what these functions are doing!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 7971/7971 [04:30<00:00, 29.44it/s]\n",
      "100%|██████████| 7971/7971 [00:16<00:00, 497.66it/s]\n"
     ]
    }
   ],
   "source": [
    "df = add_char_count_features(df.copy())\n",
    "df = get_word_stats(df.copy())\n",
    "df = get_sentiment_score(df.copy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_arr = [\"num_questions\", \n",
    "               \"num_periods\",\n",
    "               \"num_commas\",\n",
    "               \"num_exclam\",\n",
    "               \"num_quotes\",\n",
    "               \"num_colon\",\n",
    "               \"num_stops\",\n",
    "               \"num_semicolon\",\n",
    "               \"num_words\",\n",
    "               \"num_chars\",\n",
    "               \"num_diff_words\",\n",
    "               \"avg_word_len\",\n",
    "               \"polarity\"\n",
    "              ]\n",
    "feature_arr.extend(POS_NAMES.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model\n",
    "\n",
    "Now that we've added new features, let's train a new model. We'll use the same model as before, only the features are different."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We split again since we have now added all features. \n",
    "train_df, test_df = get_split_by_author(df, test_size=0.2, random_state=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, y_train = get_feature_vector_and_label(train_df, feature_arr)\n",
    "X_test, y_test = get_feature_vector_and_label(test_df, feature_arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    3483\n",
       "True     2959\n",
       "Name: Score, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1529, 7799)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = RandomForestClassifier(n_estimators=100, class_weight='balanced', oob_score=True)\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "y_predicted = clf.predict(X_test)\n",
    "y_predicted_proba = clf.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we can measure performance as we saw in the first training notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training accuracy = 0.588, precision = 0.564, recall = 0.457, f1 = 0.505\n"
     ]
    }
   ],
   "source": [
    "def get_metrics(y_test, y_predicted):  \n",
    "    # true positives / (true positives+false positives)\n",
    "    precision = precision_score(y_test, y_predicted, pos_label=True,\n",
    "                                    average='binary')             \n",
    "    # true positives / (true positives + false negatives)\n",
    "    recall = recall_score(y_test, y_predicted, pos_label=True,\n",
    "                              average='binary')\n",
    "    \n",
    "    # harmonic mean of precision and recall\n",
    "    f1 = f1_score(y_test, y_predicted, pos_label=True, average='binary')\n",
    "    \n",
    "    # true positives + true negatives/ total\n",
    "    accuracy = accuracy_score(y_test, y_predicted)\n",
    "    return accuracy, precision, recall, f1\n",
    "\n",
    "\n",
    "\n",
    "# Training accuracy\n",
    "# Thanks to https://datascience.stackexchange.com/questions/13151/randomforestclassifier-oob-scoring-method\n",
    "y_train_pred = np.argmax(clf.oob_decision_function_,axis=1)\n",
    "\n",
    "accuracy, precision, recall, f1 = get_metrics(y_train, y_train_pred)\n",
    "print(\"Training accuracy = %.3f, precision = %.3f, recall = %.3f, f1 = %.3f\" % (accuracy, precision, recall, f1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation accuracy = 0.610, precision = 0.628, recall = 0.497, f1 = 0.555\n"
     ]
    }
   ],
   "source": [
    "accuracy, precision, recall, f1 = get_metrics(y_test, y_predicted)\n",
    "print(\"Validation accuracy = %.3f, precision = %.3f, recall = %.3f, f1 = %.3f\" % (accuracy, precision, recall, f1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fortunately, this model shows stronger aggregate performance than our previous model! Let's save our new model and vectorizer to disk so we can use them later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['../models/vectorizer_2.pkl']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_path = Path(\"../models/model_2.pkl\")\n",
    "vectorizer_path = Path(\"../models/vectorizer_2.pkl\")\n",
    "joblib.dump(clf, model_path) \n",
    "joblib.dump(vectorizer, vectorizer_path) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Validating that features are useful\n",
    "\n",
    "Next, we'll use the method described in the feature importance [notebook](https://github.com/hundredblocks/ml-powered-applications/blob/master/notebooks/feature_importance.ipynb) to validate that our new features are being used by the new model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "w_indices = vectorizer.get_feature_names()\n",
    "w_indices.extend(feature_arr)\n",
    "all_feature_names = np.array(w_indices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 20 importances:\n",
      "\n",
      "num_chars: 0.0082\n",
      "num_diff_words: 0.008\n",
      "num_periods: 0.0076\n",
      "ADJ: 0.0075\n",
      "num_questions: 0.0073\n",
      "ADV: 0.0071\n",
      "PUNCT: 0.0065\n",
      "VERB: 0.0065\n",
      "num_words: 0.0064\n",
      "NOUN: 0.0064\n",
      "DET: 0.0064\n",
      "num_commas: 0.0064\n",
      "ADP: 0.0062\n",
      "PART: 0.0059\n",
      "avg_word_len: 0.0057\n",
      "polarity: 0.0056\n",
      "num_stops: 0.0055\n",
      "PRON: 0.0053\n",
      "are: 0.0049\n",
      "what: 0.0047\n",
      "\n",
      "Bottom 20 importances:\n",
      "\n",
      "fed: 0\n",
      "counted: 0\n",
      "mug: 0\n",
      "returning: 0\n",
      "multilingual: 0\n",
      "temper: 0\n",
      "growled: 0\n",
      "terrified: 0\n",
      "temple: 0\n",
      "resulted: 0\n",
      "resting: 0\n",
      "murderer: 0\n",
      "terminal: 0\n",
      "favored: 0\n",
      "cows: 0\n",
      "growth: 0\n",
      "crack: 0\n",
      "cracks: 0\n",
      "crap: 0\n",
      "imho: 0\n"
     ]
    }
   ],
   "source": [
    "k = 20\n",
    "print(\"Top %s importances:\\n\" % k)\n",
    "print('\\n'.join([\"%s: %.2g\" % (tup[0], tup[1]) for tup in get_feature_importance(clf, all_feature_names)[:k]]))\n",
    "\n",
    "print(\"\\nBottom %s importances:\\n\" % k)\n",
    "print('\\n'.join([\"%s: %.2g\" % (tup[0], tup[1]) for tup in get_feature_importance(clf, all_feature_names)[-k:]]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our new features are amongst the most predictive! On the flip side, we can see that the word vectors from the TF-IDF vectorization approach don't seem to be particularly helpful. In a following [notebook](https://github.com/hundredblocks/ml-powered-applications/blob/master/notebooks/third_model.ipynb), we will train a third model without these features and see how well it performs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing predictions to data\n",
    "\n",
    "This section uses the evaluation methods described in the Comparing Data To Predictions [notebook](https://github.com/hundredblocks/ml-powered-applications/blob/master/notebooks/comparing_data_to_predictions.ipynb), but on our new model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Bbdu28eSTT/LBBx8QEhJiO1ql+P7770lKSuLUqVNceumlREVFUb16dduxfIZrily/1H6AbnYQERH/YIxhwYIFvPDCCxw4cICnn36a6OhoV5WY7777jqSkJHJzc7n88sur7PZiF8I1Ra5v6762I4iIiJyTx+MhLS2NF154gdzcXJ599lmGDBlCtWqu+ZF9Ru3atalZsyahoaEMHTrUlf8NzsU1y4+IiIj4spKSEqZPn86LL75ItWrVeO655xgwYECVv/7tXE6dOkVwcHCVLnFafkRERMRPFRUVMXXqVP7617/SoEEDXnrpJe68807XLq2xZ88etm/fTvfu3XEch1q1atmO5NNU5ERERCzIyckhISGBV199lSuvvJJ///vf3H777a4tcADffvstKSkpFBUVcfHFF3PdddfZjuTzXFPktLODiIj4gv379/P666+TkJDAb37zGz788EPCw8s1q1al7Nixg9TUVIqLi7nhhhto166d7Uh+wd0T7yIiIpVky5YtPPDAA1xzzTWcPHmStWvXMm3aNJU4YNu2baSkpFBcXEz79u3p37+/668NPF+uGZHTSJyIiFQ2YwzLly/n73//O2vXruV3v/sd27dvp1GjRraj+Yyvv/6aDz74AI/HQ3h4OH369HH19HJZuabIiYiIVJaSkhJmzpzJP/7xDw4fPszo0aN5//33qVGjhu1oPsXj8bB06VI8Hg+dOnWiZ8+eKnFlpCInIiLiJXl5eSQmJvLKK6/QsGFDnnjiCfr3709gYKDtaD4pICCAESNGsHHjRjp16qQSVw6uKXKRKaVbc6VFpVlOIiIiVc3Jkyd5/fXXee211+jYsSPvvfceXbp0UTH5BXv37qVFixY4jkPt2rXp3Lmz7Uh+yzVFLn1buu0IIiJSxRQWFpKQkMALL7xAREQES5YsoW3btrZj+bTMzEzS09M1leolrilys4fNth1BRESqCI/HQ0pKCn/+859p06YNc+fOpX379rZj+by1a9cyb948AOrUqaMS5wWuKXKRbSJtRxARET9njGHu3Lk888wz1KxZk/fee4+IiAjbsfzCqlWrWLBgAQC9e/emY8eOlhNVDa4pciIiIhdi+fLlPPXUUxw9epS//vWv9OvXTyNK5+mzzz7j008/BeCuu+7S2nle5JoiF58ZD0BcWJzlJCIi4k++/PJLnnnmGb766ivGjRtHdHS07kItg4yMjDMlrl+/fpqC9jLXLJv8UPpDPJT+kO0YIiLiJ3bu3El0dDQ9e/akZ8+ebN26lZEjR6rElVHbtm1p0qQJAwcOVImrAK4ZkRvVYZTtCCIi4gcOHDjAX/7yF1JTU/nDH/7Av//9b+rUqWM7ll8xpnQ3JcdxqFWrFnFxcdpyq4K4psjFR8bbjiAiIj7s2LFj/OMf/+Dtt99m5MiRfP311zRu3Nh2LL9jjDlzZ+qdd96J4zgqcRXINUVORETkp4wxZGZmkpiYyLRp04iMjGT9+vW0atXKdjS/ZIwhPT2ddevWERgYSHh4OBdffLHtWFWaa4pcdk42AKF1Qi0nERER2/bv38/kyZNJTEwkPz+fkSNH8vnnn3PppZfajua3PB4PaWlpbNiwgWrVqjFs2DCVuErgmiLX/JXmAJgxxnISERGxIT8/n1mzZjFp0iRWrVrF3Xffzdtvv62ttLzA4/Ewc+ZMNm7cSFBQEFFRUVx22WW2Y7mCa4pcs9rNbEcQEZFKZoxh9erVTJo0iQ8++IAOHTpw7733Mn36dGrWrGk7XpVQUlLChx9+yObNm6levTrDhw/nkksusR3LNVxT5LJHZ9uOICIilWTv3r0kJyczadIkAEaOHMmGDRto2bKl5WRVT2FhIYcPHyY4OJgRI0bov3Elc02RExGRqi03N5ePPvqIxMREMjMzGTJkCImJiXTq1ElTpxWoRo0axMTEcOLECZo10+xXZVORExERv2WMYfny5SQmJvLhhx/SqVMnHnzwQWbPnk2NGjVsx6uyioqKWL9+PTfddNOZteJq1aplO5YruabIhcWHAZAZl2k5iYiIXKhdu3aRlJTEpEmTCAkJYeTIkWzatInQUK1MUNEKCwtJSUlh165dnDp1ittvv912JFdzTZFbt3+d7QgiInIBCgsLGT9+PG+//TYFBQUMGzaMadOmERYWpqnTSlJQUMCUKVPYu3cvtWvX5rrrrrMdyfVcU+QyRmXYjiAiIuW0fPly4uLiuPLKK1m4cCFXXHEF1atXtx3LVfLz85k8eTL79u3joosuYuTIkTRo0MB2LNdzTZELCw2zHUFERMro6NGjPPXUU8yZM4fXXnuNQYMGafTNgry8PJKTk9m/fz/16tUjNjaW+vXr244lgDY/ExERn2OMYdq0abRr147AwEA2bdrE3XffrRJnyfz589m/fz/169fn3nvvVYnzIa4ZkRu7ZGzp24ixVnOIiMiv27VrF4888gh79+5lxowZdO7c2XYk1+vZsydFRUX06tWLiy66yHYcOYtrRuTGLR3HuKXjbMcQEZFfUFxczMsvv0x4eDhdu3YlMzNTJc6i3NxcjCnd1rJmzZoMHjxYJc4HuWZEbsxtY2xHEBGRX5CRkUFcXBwNGjRg9erVXHnllbYjudrx48eZNGkSl19+OXfddZemtH2Ya4qcplRFRHxPTk4Of/7zn0lNTeUf//gH0dHRKg2WHT16lKSkJI4dO0Z2djaFhYUEBwfbjiW/wDVTqyIi4ltmz55Nu3btOH78OF999RUxMTEqcZYdPnyYxMREjh07RvPmzYmNjVWJ83GuGZHLzC7d0UHLkIiI2LV9+3aefvppvvzySxITE+nevbvtSAIcPHiQpKQkTp48SatWrRg+fLhKnB9wzYhceEI44QnhtmOIiLjWDxvZ33LLLVx//fV8+eWXKnE+4uDBg0yaNImTJ09y6aWXMmLECJU4P+GaEbkOzTrYjiAi4jrGGBYvXsxLL73E5s2bGT16NO+99x61a9e2HU3OUrt2berUqUOTJk0YNmwYQUFBtiPJeXJ+uLW4KgkPDzcZGdqSS0TEFo/Hw6xZs3jppZc4duwYTz75pEZ5fFxeXh5BQUFUq+aaMR6f4ThOpjGmXNOG+tsSERGvKSwsZMqUKYwfP546derw9NNP079/fwIDA21Hk5/Iysriq6++olevXjiOQ40aNWxHknJQkRMRkQt28uRJEhISeOWVV7j66qt56623uP3223UXqo/avXs3U6dOpbCwkCZNmtC+fXvbkaScXFPkQieEApA9OttyEhGRquPQoUO88cYbvPnmm0RERPDRRx8RHq4by3zZt99+S0pKCkVFRVx33XXccMMNtiPJBXBNkdt/cr/tCCIiVcaePXt45ZVXSEpK4u6772bFihW0bt3adiw5h2+++YZp06ZRXFzMjTfeSGRkJAEBrlnAokpyTZHb9/g+2xFERPzeli1b+Pvf/87s2bO5//772bhxI82bN7cdS87Dtm3beP/99ykpKaFDhw707dtXU99VgGuKXGidUNsRRET8ksfj4eWXX2bmzJns2LGDxx57jG+++Yb69evbjibnyRjD8uXLKSkp4aabbuLOO+9UiasiXFPkRESk7A4ePEh0dDS5ubmMGzeOW2+9lZo1a9qOJWXkOA5RUVFs2LCBTp06qcRVIa6ZGI9LiyMuLc52DBERv7Fs2TI6dOhAWFgYixcv5o477lCJ8zN79uzhh/Via9SoQefOnVXiqhjXFLmEdQkkrEuwHUNExOd5PB5efPFFhgwZQkJCAn/961+1SKwfWr9+PRMnTiQ9PZ2quPi/lHLNd+Y7fd+xHUFExOd9//33xMTEkJubS0ZGBi1atLAdScohIyODOXPmAFC/fn2NwlVhrhmRiwuLIy5MU6siIr9k6dKlP5pKVYnzT2vWrDlT4nr27EmXLl0sJ5KKZL3IOY7T23GcrY7jfOM4zlM/8/lWjuMsdhxnveM4XzqO08dGThGRquqHqdShQ4fy7rvvairVj61YsYKPP/4YgDvvvJPOnTtbTiQVzep3quM4gcCbwB1AFvC54zizjTGbzzrsOeB9Y8y/Hce5BpgLXFrWc6VtTQMgsk3khcYWEakyfphKzcvLIzMzU2vC+bENGzawcOFCAPr27UtYWJjlRFIZbI/I3Qx8Y4zZaYwpBFKB/j85xgAXnX6/LlCuPbb6pfajX2q/cgcVEalqfphKDQ8P59NPP1WJ83NXX301zZs3p3///ipxLmJ77Lw5sPesj7OAjj85ZiywwHGcx4BaQI/ynKhv677leZqISJXj8Xj429/+xhtvvMHEiRPp3bu37UhSTj/cjeo4DiEhIdx///3acstlbBe58xEFJBpjJjiO0xlIdhznWmOM5+yDHMeJA+IAWrVq9V8vkhaVVhlZRUR82pEjR4iKiiIvL4+MjAyNwvkxYwzz58+noKCAfv364TiOSpwL2f4b3we0POvjFqcfO9sDwPsAxphVQAjQ6KcvZIyJN8aEG2PCGzduXEFxRUT81/bt2+ncuTPXXnutplL9nDGGuXPnsmbNGr788ksOHDhgO5JYYrvIfQ5c5TjOZY7jVAeGAbN/cswe4DcAjuO0pbTIHazUlCIifu6zzz6ja9euPP7440yYMEF3pfoxYwxpaWlkZGQQGBjI0KFDadasme1YYonV72RjTLHjOI8C84FA4D1jzCbHcZ4HMowxs4HRQILjOP9D6Y0P95pyLFHtjCtdDNGM0erWIuIuycnJjB49msmTJ9OzZ0/bceQCeDweZs+ezRdffEG1atUYNmwYV1xxhe1YYpH1X8mMMXMpXVLk7Mf+31nvbwZurexcIiL+zhjDmDFjSE5OZvHixbRr1852JLkAHo+Hjz76iK+++oqgoCCioqK47LLLbMcSy6wXucqikTgRcZP8/Hzuv/9+du7cyerVq2nSpIntSHKBioqKOHr0KNWrV2f48OFccskltiOJD3BNkRMRcYuDBw8yYMAAmjdvzuLFi6lRo4btSOIFwcHBREdHc+TIEUJDQ23HER9h+2YHERHxoi1bttCpUyciIiJITU1VifNzxcXFrFq1Co+ndMWtkJAQlTj5EdeMyEWmlG7NpfXkRKSqWrRoEcOHD2f8+PHce++9tuPIBSoqKiI1NZWdO3dy4sQJevXqZTuS+CDXFLn0bem2I4iIVJj//Oc/PPPMM0ybNo2IiAjbceQCFRYWkpKSwq5du6hVqxbt27e3HUl8lGuK3OxhP12eTkTE/3k8Hp555hlmzJjBZ599RuvWrW1HkgtUUFDAlClT2Lt3L3Xq1CE2NpZGjf5rHXwRwEVFLrJNpO0IIiJelZubS0xMDAcPHmTVqlX6YV8F5OXlMWXKFPbt28dFF13EyJEjadCgge1Y4sN0s4OIiB/av38/t912GzVr1uSTTz5RiasiFi1axL59+6hXrx733XefSpyck2uKXHxmPPGZ8bZjiIhcsI0bN9KpUyf69etHUlISwcHBtiOJl9xxxx1ce+213HvvvdSrV892HPEDTjl2u/J54eHhJiMj40ePaYsuEakK5s2bR2xsLK+//jpRUVG244gX5ObmEhISQkCAa8ZW5Cccx8k0xoSX57muuUZuVIdRtiOIiFyQN998kxdeeIGZM2dy663aubAqOHHiBJMmTaJFixb0799fZU7KzDVFLj5S06oi4p9KSkoYPXo08+fPZ/ny5dokvYo4duwYSUlJHD16lKCgIAoLCwkJCbEdS/yMa4qciIg/OnnyJFFRUeTm5rJy5Urq169vO5J4wdGjR5k0aRLHjx8nNDSU6OholTgpF9eM4WbnZJOdk207hojIecvKyqJr1640adKEjz/+WCWuijh8+DATJ07k+PHjtGjRgpiYGG2lJuXmmiLX/JXmNH+lue0YIiLnZfny5XTu3JmoqCgSEhIICgqyHUm84PDhwyQmJpKTk0OrVq00EicXzDVTq81qN7MdQUTknIqKinj++edJSEggISGByEgtZl6V1K5dm3r16tGoUSOioqKoXr267Uji51xT5LJHa1pVRHzb9u3biY6Opn79+mzYsIGmTZvajiReFhwczIgRIwgMDNQoq3iFa6ZWRUR8lTGG//znP9xyyy2MGDGCuXPnqsRVIfv27SM9PR2PxwNASEg9tF4pAAAgAElEQVSISpx4jWtG5EREfNHhw4eJi4vjm2++YfHixVx77bW2I4kX7d27l8mTJ1NYWMjFF1/MzTffbDuSVDGuGZELiw8jLD7MdgwRkTMWLlzIDTfcwCWXXMKaNWtU4qqY3bt3k5ycTGFhIe3atSMsTD+DxPtcMyK3bv862xFERAAoKCjg2WefJTU1lYkTJ3LHHXfYjiRetnPnTlJSUiguLub666/Xrg1SYVxT5DJGZZz7IBGRCrZp0yZGjBjB5ZdfzoYNG2jUqJHtSOJl33zzDdOmTaO4uJgbb7yRyMhIlTipMK75lxUWGkZYqIa1RcQOYwxvvPEGERERPProo8yYMUMlrgoyxrBq1SqKi4sJCwujX79+KnFSoVwzIiciYst3333H/fffz8GDB1m5ciVXXXWV7UhSQRzHYciQIaxfv56OHTviOI7tSFLFuebXhLFLxjJ2yVjbMUTEZdLT07nxxhtp3749K1asUImronbv3n1meZHg4GA6deqkEieVwjUjcuOWjgNgbMRYu0FExBVyc3P53//9X+bOncv7779P165dbUeSCvLFF18wa9Ys2rVrx6BBg1TgpFK5psiNuW2M7Qgi4hLr169n+PDhhIWF8cUXX1C3bl3bkaSCrFu3jrS0NAAaNWqkEieVzjVFTiNxIlLRPB4PL7/8Mi+//DKvvvoqw4cPtx1JKtDnn3/O3LlzAfjNb35Dly5dLCcSN3JNkRMRqUh79+5l5MiRFBcXs3btWi699FLbkaQCrV69mvnz5wPQs2dPOnfubDmRuJVrbnbIzM4kMzvTdgwRqYI++OADwsPD6dGjB4sXL1aJq+K++uqrMyXuzjvvVIkTq1wzIheeEA6AGWMsJxGRqiInJ4fHHnuMlStXkp6ezk033WQ7klSC1q1bc8kll3Dddddp2y2xzjVFrkOzDrYjiEgVM2LECOrVq8e6deuoXbu27ThSgYwxGGMICAigevXqxMbGaqFf8QmuKXKZcZpWFRHviY+PZ/Xq1SpxLmCMYeHChRw7doy7776bgIAAlTjxGa4pciIi3lBQUMBjjz3GihUrWL58OS1atLAdSSqQMYb58+ezZs0aAgIC2LdvHy1btrQdS+QMFTkRkfO0b98+7rnnHkJDQ1m9ejV16tSxHUkqkDGGuXPnkpGRQUBAAIMHD1aJE5/jmrHh0AmhhE4ItR1DRPzU8uXLufnmm+nXrx/Tp09XiavijDGkpaWRkZFBYGAgw4YN4+qrr7YdS+S/uGZEbv/J/bYjiIgfMsbw1ltv8fzzzzNp0iR69+5tO5JUMI/Hw+zZs/niiy+oVq0aUVFRXH755bZjifws1xS5fY/vsx1BRPxMfn4+Dz/8MJmZmaxcuZIrrrjCdiSpBCUlJRw/fpygoCCGDx+udQHFp7mmyIXW0bSqiJy/vXv3MmjQIK644gpWrVpFrVq1bEeSShIUFERUVBSHDx+mWbNmtuOI/CrXXCMnInK+lixZws0338zQoUNJSUlRiXOB4uJili9fTklJCQDVq1dXiRO/4JoRubi0OADiI+MtJxERX2WM4bXXXuOll15i8uTJ9OjRw3YkqQRFRUVMmzaNHTt2cPToUSIjI21HEjlvrilyCesSABU5Efl5ubm5xMXFsWnTJlavXq3rolyisLCQ1NRUvv32W2rWrMnNN99sO5JImbimyL3T9x3bEUTER+3atYuBAwfSrl07VqxYQc2aNW1HkkpQUFDA1KlT2bNnD7Vr1yY2NpbGjRvbjiVSJq4pcnFhcbYjiIgPWrhwIdHR0Tz99NP8/ve/x3Ec25GkEuTn5zNlyhSysrKoU6cOI0eOpGHDhrZjiZSZa4qciMjZjDFMmDCBV155hdTUVCIiImxHkkq0dOlSsrKyqFu3LrGxsTRo0MB2JJFycU2RS9uaBkBkG13EKuJ2mzdv5pFHHiEvL481a9Zo2yUX6t69O3l5eURERFCvXj3bcUTKzTXLj/RL7Ue/1H62Y4iIRadOneKpp57itttuY/DgwaxcuVIlzkVyc3PPLC8SFBTEgAEDVOLE77lmRK5v6762I4iIJcYYZs2axR/+8Ae6devGxo0badq0qe1YUolycnJISkqiUaNG3HPPPQQGBtqOJOIVrilyaVFptiOIiAXffvstjz32GDt27CAxMZHbb7/ddiSpZCdOnGDSpEkcOXKEgIAACgoKdGeyVBmumVoVEXcpKCjgxRdf5KabbqJLly588cUXKnEudOzYMSZOnMiRI0do2rQpI0eOVImTKsU1I3Ii4h6LFi3ikUceoW3btmRkZGhxX5c6cuQISUlJHD9+nNDQUKKjo6lRo4btWCJe5Zoi54wrXRvKjDGWk4hIRdm/fz+jR49m1apVvP7669pqycWOHj1KYmIiOTk5tGjRghEjRhASEmI7lojXaWpVRPxecXExr7/+Otdffz2XXnopmzZtUolzudq1a9OoUSMuueQSoqOjVeKkynLNiJxG4kSqptWrV/Pwww9Tv359li1bRtu2bW1HEh8QFBTEsGHDAKhevbrlNCIVRyNyIuKXjhw5wkMPPcSgQYP405/+xKJFi1TiXG7//v3MnDnzzFpx1atXV4mTKk9FTkT8isfjYeLEiVxzzTUEBwezefNmhg8frj1SXS4rK4tJkybxxRdfsHr1attxRCqNa6ZWI1NKr5fRenIi/mvjxo08/PDDFBYWMmfOHMLCwmxHEh+wZ88epkyZQmFhIW3btqVTp062I4lUGtcUufRt6bYjiEg55eTkMG7cOJKSkvjLX/7Cgw8+qJX5BYBdu3YxdepUioqKuPbaaxk4cCABAZpsEvdwTZGbPWy27QgiUg7Hjh2jW7du3HjjjXz11VdcfPHFtiOJj9i5cycpKSkUFxdz/fXX079/f5U4cR3XFLnINlqKQMTf5OfnM2DAAG6//XZeffVVXQcnP/L5559TXFxM+/bt6du3r0qcuJJripyI+JeSkhJiYmK4+OKL+ec//6kSJ/9l0KBBrFu3jptvvln/PsS1XPPrS3xmPPGZ8bZjiMh5MMbwxz/+kUOHDpGUlKSRFjlj165dFBcXA6VrxXXs2FElTlzNNf93fCj9IR5Kf8h2DBE5D+PHj2fZsmXMnDlTK/LLGRs3biQpKYnp06fj8XhsxxHxCa6ZWh3VYZTtCCJyHhITE3n77bdZuXIldevWtR1HfMSGDRuYNWsWAE2aNNEonMhprily8ZGaVhXxdfPmzeOpp55iyZIlhIaG2o4jPiIzM5P09NIlpG6//Xa6detmOZGI73BNkRMR37Z27VpiY2OZPXs2V199te044iPWrl3LvHnzAOjRowe33nqr5UQivsU1RS47JxuA0Dr6LV/E12zfvp3+/fvz3nvv0blzZ9txxEd8/fXXZ0pcr169tGODyM9wTZFr/kpzAMwYYzmJiJztwIED9O7dm7/85S9ERmq9R/k/V155JVdccQVt2rThpptush1HxCe5psg1q93MdgQR+YkTJ07Qp08f7r33Xh588EHbccRHeDweAgICqFatGiNGjNCNDSK/wjVFLnt0tu0IInKWwsJCBg0aRMeOHXnuuedsxxEfYIxh8eLFHDhwgCFDhlCtWjWVOJFzcE2RExHf4fF4uPfee6lTpw5vvPGGflgLxhg++eQTVq1aheM4ZGVlcemll9qOJeLzyrwgsOM49zmOs9BxnH2O4xw76/HrHcf5u+M4l3s3oohUNU888QR79uxh6tSpBAYG2o4jlhlj+Pjjj1m1ahUBAQEMHjxYJU7kPJ33iJzjONWAj4A+QB6QC9Q565B9wB+Ak8DzXszoFWHxYQBkxmVaTiLibhMmTGDevHl89tln1KhRw3YcscwYw5w5c8jMzCQwMJAhQ4bQunVr27FE/EZZRuQeB+4CXgbqA2+d/UljzGFgOdDba+m8aN3+dazbv852DBFXmzp1Kq+99hoff/wxDRo0sB1HLPN4PMyePZvMzEyqVavGsGHDVOJEyqgs18jFAGuNMU8COI7zc+t47KC07PmcjFEZtiOIuNonn3zC//zP//Dpp5/SsmVL23HEBxhjOHXqFEFBQURFRXHZZZfZjiTid8pS5K4E3jzHMYeAhuWPU3HCQsNsRxBxreXLlzN8+HA+/PBD2rVrZzuO+IgfplIPHjxIs2ZaIkqkPMoytVrAj6+J+zmtgBPljyMiVc3GjRu5++67mTp1Kl27drUdRywrKSlh2bJlFBcXA1CtWjWVOJELUJYRuS+AHo7jBBljin76ScdxagN3AD45hzl2ydjStxFjreYQcZNdu3Zx55138tprr3HHHXfYjiOWFRcX8/7777N9+3YOHTrEoEGDbEcS8XtlGZGbCFwGvOs4TsjZn3AcpyYQDzQ6/dbnjFs6jnFLx9mOIeIahw4dolevXjzxxBMMGzbMdhyxrKioiNTUVLZv306NGjW0p66Il5z3iJwxJtFxnN6U3vQwCDgK4DjOEqA9pdOuicaYmRWQ84KNuW2M7QgirnHy5En69OnDPffcw+9//3vbccSywsJCUlJS2LVrFzVr1iQ2NpYmTZrYjiVSJTjGlG0TecdxHqV0vbgrznp4N/CKMeZfXsxWbuHh4SYjwydneEWqvMLCQiIjI2nRogXvvvuudm1wuYKCAqZOncqePXuoXbs2sbGxNG7c2HYsEZ/iOE6mMSa8PM8t8xZdxpg3gDccx2lI6R2qx40x35Xn5CJStXg8Hu6//35CQkJ45513VOKE5cuXs2fPHurUqcPIkSNp2NAnFzYQ8Vtl2dmhA5BtjDkAZxYAPvyTY5oAzY0xPrfybmZ26Y4OWoZEpGIYY/jf//1fdu/ezYIFC6hWTVs5C9x2223k5ubSpUsX6tevbzuOSJVTlpsdPgfiznHMg6eP8znhCeGEJ5Rr1FJEzsM//vEPFixYwOzZs7X1lsvl5uZSVFS6uEG1atWIjIxUiROpIGX5lfl85kh8dh6lQ7MOtiOIVFmJiYm89dZbrFixQj+wXe7kyZMkJSVx0UUXMWzYMI3MilQwb3+HNQdyvPyaXpEZl2k7gkiVNGfOHJ566imWLFlC8+bNbccRi3JyckhKSuLQoUNA6Y0OKnIiFetXv8Mcx3n8Jw/d8jOPAQRSuqtDDD46tSoi3rdy5Uruvfde0tPTufrqq23HEYuOHz9OUlISR44coUmTJsTExFCrVi3bsUSqvHP9qvQyYCidMjVAz9N/fskh4FnvRBMRX7Z582YGDhxIcnIyHTt2tB1HLDp27BiTJk3i2LFjNGvWjOjoaGrWrGk7logrnKvIRZ5+6wCzgalAys8cV0LpHaxfGGMKvRfPe0InhAKQPTrbchIR/7d371569+7NhAkT6N27t+04YtGJEyeYOHEiJ06coHnz5kRHRxMSEnLuJ4qIV/xqkTPGzPnhfcdxZgAzz37Mn+w/ud92BJEq4fDhw/Tq1Ys//vGPREdH244jltWqVYumTZtSt25dRowYQXBwsO1IIq5S5p0d/MHP7eyQnVM6EhdaJ9RGJJEq4dSpU/To0YOuXbvy97//3XYc8RHFxcV4PB6qV69uO4qIX7qQnR3Kso6cXwutE6oSJ3IBioqKGDp0KG3atGH8+PG244hFBw4cYPr06T9aK04lTsSOMt0XfnpbrieAXpQuNfJzY+jGGFPXC9lExEcYYxg1ahQACQkJ2nrLxbKzs0lOTiY/P5/GjRtz22232Y4k4mpl2aKrCbAauAT4ltJ9VvdTejfrD0NdXwOnvJzRK+LSSjeliI+Mt5xExP889dRTbN26lUWLFhEUFGQ7jliSlZXF5MmTKSgooE2bNtx66622I4m4XlmmVv8fpWvFDTDGXHH6sXeMMS2ANsBSoADo7t2I3pGwLoGEdQm2Y4j4nQkTJpCWlkZ6erqWlHCxPXv2kJycTEFBAddccw2DBw/WYr8iPqAs34V3AguNMbN/+gljzHbHcQYAm4CxwGjvxPOed/q+YzuCiN+Jj4/njTfeYNmyZTRs2NB2HLHk22+/JSUlhaKiIq699loGDhxIQIBrLrEW8WllKXKhwIyzPi4BziwWZIw57jjOfGAQPljk4sLibEcQ8StTp07l+eefZ8mSJbRs2dJ2HLFo/fr1FBUVccMNN9CvXz+VOBEfUpYil0PpVlw/OMb/XRv3gyNAkwsNJSJ2zZo1i8cff5xFixZx5ZVX2o4jlvXv35+WLVsSHh6uG11EfExZfq3aA5z9a/lG4HbHcc6+c7U7sM8bwbwtbWsaaVvTbMcQ8XkLFy5k1KhRpKen065dO9txxJJdu3adWV4kMDCQm266SSVOxAeVpch9CkQ4jvPDKN5kSovdEsdxxjiOswi4EfjQyxm9ol9qP/ql9rMdQ8SnrVy5kuHDhzNjxgzCw8u1NqVUAZs2bSI5OZnU1FRKSkpsxxGRX1GWqdX3gHxKp073AROBzsADwA87ZqcDz5clgOM4vYHXKJ22fdcY89LPHDOE0psoDKX7uQ4vyzkA+rbuW9aniLjK+vXrGThwIMnJyXTt2tV2HLHkyy+/ZObMmRhjaNasma6HE/FxF7xFl+M4lwBXAruMMTvK+NxAYBtwB5AFfA5EGWM2n3XMVcD7QHdjzFHHcS42xnz/a6/7c1t0icgv27JlC927d+fNN99k0KBBtuOIJRs2bGDWrFkAdOvWjYiICE2nilSCC9miqywLAg8CDhtjlp79uDFmN7C7PCcHbga+McbsPH2OVKA/sPmsY0YBbxpjjp4+36+WOBEpm2+//ZaePXsyfvx4lTgXy8zMJD09HYDbb7+dbt26WU4kIuejLGPm7wN3e/n8zYG9Z32cdfqxs7UGWjuOs8JxnNWnp2JFxAv27dtHjx49ePrpp4mNjbUdRyzZvn37mRJ3xx13qMSJ+JGyXCP3PeCpqCC/ohpwFRABtACWOY5znTHm2NkHOY4TB8QBtGrV6r9exBlXOj1gxlzYVLJIVXHw4EHuuOMO4uLieOSRR2zHEYsuv/xy2rZtyyWXXELHjh3P/QQR8RllKXILAW//mraPHy9p0oL/Xr4kC1hjjCkCvnUcZxulxe7zsw8yxsQD8VB6jZyXc4pUKcePH6dXr14MHDiQJ5980nYcsaSkpITAwEACAwMZPHiwrocT8UNlmVp9BmjiOM6bjuPU8dL5PweuchznMsdxqgPDgJ9uATaT0tE4HMdpROlU686ynsiMMRqNEwFOnTrFXXfdRZcuXXjhhRdsxxELjDEsWbKE5OTkM2vFqcSJ+KeyjMi9Relo2W+BGMdxtgIHKF0S5GzGGNP/fF7QGFPsOM6jwHxKlx95zxizyXGc54GM0/u6zgd6Oo6zmdJtwf5kjDlchtwiclp+fj4DBw7kqquu4tVXX9UPbxcyxvDpp5+yfPlyHMdh9+7d2r1DxI+d9/IjjuOc7/VxxhgTeO7DKo6WHxH5b0VFRQwePJjq1auTkpJCYKDVb1OxwBjDggULWL16NY7jMGjQIK699lrbsURcr1KWHwG8NZ1qRWRKJABpUdqmS9zH4/Fw3333UVhYyPvvv68S50LGGObNm8fnn39OQEAA99xzD23btrUdS0Qu0HkXOWPMqYoMUtHSt6XbjiBihTGGRx55hKysLObNm0f16tVtR5JKZowhPT2ddevWERgYyJAhQ2jdurXtWCLiBWUZkfNrs4f99B4KkarPGMMTTzzB+vXrWbhwITVq1LAdSSwwxpCfn0+1atUYOnSorokTqUJcU+Qi20TajiBS6V544QU+/vhjli5dSp06fn11hFyAgIAABg0axPfff0+zZs1sxxERL9JuyCJV1KuvvkpycjKffPIJDRo0sB1HKllJSQlLly6lsLAQgMDAQJU4kSrINSNy8ZnxAMSFxVlOIlLxjh49ynPPPcfmzZtp2rSp7ThSyYqLi5k+fTpbt27lwIEDDB061HYkEakgrilyD6U/BKjIiTuMHz+eiIiIn92uTqq24uJi3n//fbZv305ISAhdu3a1HUlEKpBrityoDqNsRxCpFNOnTyc1NRWtpeg+RUVFpKamsnPnTmrUqEFsbKxGZEWqONcUufjIeNsRRCrcpk2bePjhh5k/fz6NGjWyHUcqUWFhISkpKezatYtatWoRGxvLxRdfbDuWiFSwMhc5x3EuAvoBbYHaxpg/nH68LtAc+MYYU+jVlCJyTseOHWPAgAFMmDCBDh062I4jlWz16tXs2rWL2rVrM3LkSBV5EZcoU5FzHGcY8Daluzw4lO6z+ofTn74cyADuByZ5MaNXZOdkAxBaJ9RyEhHv83g8xMTEcOeddxIbG2s7jljQpUsXTp48SceOHWnYsKHtOCJSSc57+RHHcW4DJgP7gRjgP2d/3hizHtgKDPRmQG9p/kpzmr/S3HYMkQrx/PPPc/z4cSZMmGA7ilSivLw8CgoKgNK14vr06aMSJ+IyZRmRexo4BNxijDnqOM5VP3PMeuBmryTzsma1tX6SVE2zZ8/mP//5D59//jlBQUG240glOXXqFMnJyQQHBzNixAhtvSbiUmUpcjcD040xR3/lmCyg/4VFqhjZo7NtRxDxuq1bt/Lggw8ye/Zs3Z3oIidPniQpKYmDBw/SsGFDCgoKVOREXKosRa4GcOIcx9Sh9Lo5EalgOTk5DBw4kBdffJFOnTrZjiOV5MSJEyQlJXH48GEaN25MbGwstWvXth1LRCwpS5HbDdx4jmNuAraXP46InA9jDPfddx9dunRh1CitkegWx48fZ9KkSRw9epQmTZoQExNDrVq1bMcSEYvKstdqOnC74zh3/dwnT9/RGgZ85I1g3hYWH0ZYfJjtGCJeMX78eLKysvjXv/5lO4pUkpycHCZOnMjRo0dp1qwZI0eOVIkTkTKNyL0EDAM+chwnGbgYwHGckUA3IBr4FnjN2yG9Yd3+dbYjiHjF/Pnzef3111m7di3BwcG240glqVWrFi1btqROnTqMGDGCkJAQ25FExAecd5EzxhxyHKc7MBW476xPvUfpmnLrgMHGmHNdR2dFxihtVyT+b+fOncTGxvLBBx/QokUL23GkEgUEBDBw4ECKi4t1Y4OInFGmBYGNMduAcMdxbgE6Aw2B48BqY8zSCsjnNWGhmlYV/5abm8ugQYN47rnn6Natm+04Ugm+//57Fi9ezIABAwgODiYgIEAlTkR+pFx7rRpjVgIrvZxFRH6BMYZRo0Zx/fXX8+ijj9qOI5Vg//79JCcnk5eXx2effUaPHj1sRxIRH3TeRc5xnP8HJBpj9lRgngozdsnY0rcRY63mECmPV199lS1btrBixQocx7EdRyrYvn37mDx5Mvn5+Vx11VVERETYjiQiPsox5vyWfXMcxwN4gGWU7qU63RhzqgKzlVt4eLjJyPjxNXHOuNIffmaMlrkT/7J48WKioqJYvXo1l156qe04UsH27t3LlClTKCgooE2bNtxzzz1Uq1auyRMR8ROO42QaY8LL89yy/N9hFDASuO30nzccx5kBTDLGLC7PySvTmNvG2I4gUmZ79+5l+PDhTJ48WSXOBXbv3s3UqVMpLCzkmmuuYdCgQQQGBtqOJSI+7LxH5M48wXEuo7TQxQCXUbqTw14gCUgyxnzj7ZBl9XMjciL+Ji8vj27dujFkyBD+9Kc/2Y4jlSAtLY1169Zx3XXXMWDAAAICyrLUp4j4qwsZkStzkfvJibtRWuru4f+251pljOlS7hf1AhU58Xcej4chQ4YQHBzM5MmTdV2cS3g8HtavX0/79u1V4kRc5EKK3AX9n8IYs8wY8wDQBHgSKKZ0WRKfk5mdSWZ2pu0YIufliSee4ODBg7z33nsqcVXcrl27yM/PB0rXigsLC1OJE5HzdkFX0DqOEwIMonRUrjsQCOR5IZfXhSeUFl3d7CC+7s033yQ9PZ2VK1dq54YqbsuWLUyfPp3Q0FBiY2MJCgqyHUlE/Ey5ipzjOF358ZSqA6ym9G7WVK+l86IOzTrYjiByTmlpabz44ousWLGCBg0a2I4jFWjTpk3MmDEDYwwtW7bUnakiUi5lWUfuMiD29J9LKS1v+4C3KF1fbltFBPSWzDhNq4pvy8jI4IEHHmDOnDlcdtlltuNIBfryyy+ZOXMmxhi6dOlC9+7dNYUuIuVSll8Bd1B6M0M+paNuicBCcyF3S4gIUHqdVP/+/UlISOCmm26yHUcq0Pr165k9ezYAERERdOvWTSVORMqtLEVuJaXlbZoxJqdi4oi4z9GjR+nTpw9PPfUU/fv3tx1HKtCuXbvOlLju3bvTtWtXy4lExN+dd5GzvaTIhQqdEApA9uhsy0lE/k9BQQEDBw6kd+/ePPbYY7bjSAVr1aoV119/PU2bNqVzZ5+8wV9E/Ixrrq7df3K/7QgiP2KM4YEHHqBhw4a8/PLLtuNIBSouLqZatWoEBAQwYMAATaWKiNf8YpFzHOd1Sq+Je8EYc/D0x+fDGGP+4JV0XrTv8X22I4j8yJ///Gd27NjBp59+qnXDqrBly5axbds2YmJiCA4OVokTEa/6tRG5Ryktcm8CB09/fD4M4HNFLrROqO0IIme8++67pKamsmrVKmrUqGE7jlQAYwxLlixh2bJlQOn1cW3atLGcSkSqml8rctedfrvzJx+LyAWYP38+zz33HMuWLaNx48a240gFMMawaNEiVqxYgeM4DBgwQCVORCrELxY5Y8ymX/vY38SlxQEQHxlvOYm42RdffEFMTAwfffQRrVu3th1HKoAxhvnz57NmzRoCAgIYNGgQ7dq1sx1LRKqo874wx3Gcxx3H6XSOYzo6jvP4hcfyvoR1CSSsS7AdQ1wsKyuLvn378q9//Ytbb73VdhypAMYY5s6de6bEDR48WCVORCpUWe5afRkYS+lWXL+kB/A88MoFZKoQ7/R9x3YEcbETJ05w11138dhjjzF06FDbcaQCFRcXExgYyNChQ7nqqqtsxxGRKs7by49UAzxefk2viAuLsx1BXKqoqIh77lqwRHwAACAASURBVLmHW265hT/96U+240gFchyHyMhIOnbsSNOmTW3HEREX8PaaB9cBR7z8miJ+yxjDww8/TPXq1fnXv/6lpSeqII/Hw+LFi8nPzwcgICBAJU5EKs2vjsg5jjP7Jw8Ndxwn/GcODQRaAdcA072UzavStqYBENkm0nIScZO//vWvrF+/nqVLl1KtmmvW33aNkpISZsyYwZYtW9i3bx/R0dG2I4mIy5zrJ0vfs943QOvTf35OHjAH+KMXcnldv9R+AJgxxnIScYspU6aQkJDAqlWrqF27tu044mXFxcVMnz6drVu3EhwcTEREhO1IIuJC5ypydU6/dYATwF+Bv/3McSXGmHxvBvO2vq37nvsgES9ZsmQJjz/+OJ9++inNmjWzHUe8rKioiPfff59vvvmGkJAQYmJiCA3VouMiUvl+tcgZY0798L7jOI8Ba85+zJ+kRaXZjiAusWXLFoYOHUpKSoqWnqiCioqKSE1NZefOndSsWZOYmBhdEyci1vx/9u47vooq8f//awgJvQgIUkSKgECAQAIq4oIrxYWEolSBAK4EdXX3t4tYVpHirqIu1hUl+lEIAaRIjfhFadKFBBECSBGRkgChCaSS5Pz+CGZBKUm4ybnJvJ+PBw+4c+fOvMFI3pyZOSfHN+0YY97PzyAiRcHRo0fp2rUrb7zxBn/84x9tx5F8EB0dzf79+ylTpgyhoaFUrVrVdiQRcbGrFjnHcVpd/GWsMSbtktfXZYzZcsPJRAqZxMREgoODGTZsGKGhobbjSD656667OHfuHK1ataJKlSq244iIyznGXPnmf8dxMsl6wKGxMWbPJa+vyxjj47mIuRcUFGSio6Mv2+aMy5r2QQ87SH7IyMigV69eVK5cmU8++UTTjBQxycnJAJQqVcpyEhEpihzHiTHGXGlWkOu61qXVN8kqbid/81pELpGamsrIkSNJSkpi7ty5KnFFTFJSEtOmTcNxHEJDQylZsqTtSCIi2a5a5IwxT1/rdWGjkTjJDzt27KBdu3Y0bNiQr776Cj8/P9uRxIMSExOJiIjg+PHjVKpUibS0NBU5EfEqnl7ZQcQ1zp8/T58+fXjzzTf59ttvqVChgu1I4kHnzp1jypQpHD9+nCpVqjB06FDKly9vO5aIyGVueKp5x3HuBLoDScAnxpj4G04l4uWMMTzxxBPceeedDBs2zHYc8bCzZ88ydepUTp06xc0330xoaKgmdRYRr5TjIuc4zn+Ap4DqxphTF7f1BOaQtUQXwJOO47TyxjIXMjNraS7NJyee8H//939s2bKFTZs22Y4iHpaUlMSUKVM4ffo01apVY/DgwZQpU8Z2LBGRK8rNiFx74JtfS9xFLwOJwHPALcBo4B/AKI8l9JCoPVG2I0gR8f333/P888+zevVqSpcubTuOeFipUqWoU6cOpUqVYtCgQXpSVUS8Wm6KXG3g219fOI5TB2gKvGqM+fDitjZAV7ywyC3qv8h2BCkCzp49S58+fXjnnXdo3Lix7TiSDxzHISQkhLS0NEqUKGE7jojINeXmYYcKwJlLXrclazqSJZds2wbc6oFcHhfSKISQRiG2Y0ghZoxh+PDh3HfffTz88MO244gHJSQkMGPGjOz54hzHUYkTkUIhNyNyR4HbLnl9P5AKbL5kWykg0wO5RLzOBx98wJ49e9iwYYPtKOJBx44dIyIigqSkJFatWsWf/vQn25FERHIsN0UuGujuOE4HIAXoA6w0xqRdsk9dIM5z8TwnPCYcgLDAMMtJpDCKiYlhzJgxbNiwQfOIFSHx8fFMmzaN5ORk6tevT8eOHW1HEhHJldwUuQlACLD8km2v/foLx3H8gHaAV96MNiJqBKAiJ7l35swZ+vbty6RJk7j99tttxxEPOXLkCJGRkaSkpNCgQQP69u1L8eI3PCOTiEiByvHfWsaYaMdx7gUeubhppjFm9SW7BAExwGwP5vOY4a2G244ghZAxhmHDhtGtWzf69OljO454yKFDh5g+fTqpqanccccd9O7dGx8fq0tEi4jkSa7++WmM2QRcceIsY8x6oJMnQuWH8JBw2xGkEHr77bc5cuQIn332me0o4kE7d+4kNTWVpk2b0qtXL5U4ESm08nwdwXEcH6A8cM4Yk+65SCLeYePGjUyYMIGNGzfqCcYipnPnzlStWpUWLVpQrJhWKhSRwitXf4M5WZ50HGcrWU+sngBSHMf57uJ2J19SekDcuTjiznnlcxjihU6ePEm/fv0IDw+nbt26tuOIBxw4cICkpCQga3qRli1bqsSJSKGX47/FHMcpDiwG3gGakzWn3M6LP7e4uP2Li/t5nZpv1qTmmzVtx5BCIDMzk9DQUPr06UOPHj1sxxEP2L17N5GRkUybNo20tLTrf0BEpJDIzT9H/0rWqg0rgABjTBVjTDNjTBUggKynWbtc3M/rVC9bneplq9uOIYXAG2+8wenTp3n11VdtRxEP2LlzJ7NnzyYjI4PatWvj6+trO5KIiMc4xpic7Zh1ObUE0OxK98RdHInbDqQaYwI8mjKXgoKCTHR0tM0IUkitWbOGPn36sHnzZm691SsXKZFc2L59O/Pnz8cYQ9u2benYsSNefAeIiLiU4zgxxpigvHw2NyNyDYDFV3uw4eL2xRf3Eyl0jh8/zoABA/j0009V4oqA77//PrvE3XvvvSpxIlIk5eZ+tnTgelPalwQy8h5HxI6MjAwGDRpEaGiolmgqAg4dOsSCBQsA6NChA+3bt7ecSEQkf+SmyMUCvR3HeckYc+a3bzqOUwF4iKzLq14nMDwQgJiwGMtJxBt9/PHHJCYmMn78eNtRxANq1apFYGAgFStWpF27drbjiIjkm9wUuQ+BqcBGx3HGkLXO6nHHcW4GOgBjgVuA5zwd0hO2xG+xHUG81Llz5xg7dixffPGFlmgq5NLT0ylevDiO49CtWzddShWRIi83S3RNcxynNfAkMAPAcZxM/nefnQNMMsZM83hKD4gerocf5Mpef/11OnbsSKtWrWxHkRuwbt06tm/fTmhoKKVLl1aJExFXyO0SXX91HGcxWeuttgQqAL8A3wGfGGO+9nxEzwisEWg7gnihI0eOMGnSJL777jvbUeQGfPPNN6xatQrImvi3SZMmdgOJiBSQXF9HuljWvLawieTG6NGjCQsLo3bt2rajSB4YY1i5ciVr1qwBoEePHipxIuIqrrkhaOyqsVk/dxhrNYd4j23btvHFF1+wZ88e21EkD4wxLFu2jPXr1+M4Dr169aJZs2a2Y4mIFKhcFznHcVoCQ/n9pdUpxhivvT417ptxgIqc/M+oUaMYPXo0FSpUsB1FcskYw9KlS/n2228pVqwYDz30kEbiRMSVclXkHMd5FRjF7ycSbgf8xXGc140x//RUOE8a036M7QjiRZYuXcpPP/3EiBEjbEeRPMrMzKRYsWL06dOHO+64w3YcERErcrNEVxhZU5AcASYAq4CjZE05ch9Z045UB0YYYz7Oj7A5pSW65FoyMjJo2bIl48aNo1evXrbjSB4ZYzh+/DjVqlWzHUVE5IYU1BJdTwLHgVbGmPeNMTuMMScv/vxfIBA4Afw1L0FECsrUqVMpX748PXv2tB1FciEzM5OVK1eSmJgIgOM4KnEi4nq5XWt1jjEm4UpvGmOOAXPw0rVWY+JiiInTqg5ul5iYyEsvvcTEiRM1z1ghkpGRwfz581m9ejWzZs0ip1cSRESKutzcI3caSLrOPknAqbzHyT9BH2WNWJox+gbgZm+++Sbt2rXjzjvvtB1FcigjI4PPP/+cXbt24efnR8eOHVXCRUQuyk2RiwKCHcf5pzEm47dvOo5THAi+uJ/XaVVds/a73dGjR3n77bfZvHmz7SiSQ+np6cyZM4c9e/ZQokQJBg8eTM2aNW3HEhHxGrl52KESWQ84HAGeNcZsu+S9FsBrQA2gvTHmtOej5pwedpAreeyxxyhTpgwTJ060HUVy4MKFC8yaNYsff/yRUqVKMXjwYKpXr247loiIx93Iww65GZFbBZQCOgOdHcc5BxwDqgHlLu6zH/jmN5c9jDGmRV7CiXjKzp07+fzzz9m9e7ftKJJD27Zt48cff6R06dKEhobqwQYRkSvITZGrARguvweuEnDhkm0VL/4Q8SrPPvsszz//PJUqVbIdRXKoVatWnD17Fn9/f26++WbbcUREvFKOi5wxpkp+BslvNSbWACBuZJzlJFLQVq5cyY4dO5g7d67tKHIdKSkppKenU7ZsWRzH4b777rMdSUTEq7lmrdX48/G2I4gFmZmZPP3007z66quUKFHCdhy5huTkZCIjI7lw4QJDhgyhTJkytiOJiHg91xS5I/84YjuCWDBjxgx8fX3p27ev7ShyDUlJSUybNo2jR49SsWJFLly4YDuSiEih4JoiV6NcDdsRpIAlJyfzz3/+kxkzZmjeMS+WmJhIREQEx48fp1KlSgwZMoTy5cvbjiUiUii4psiJ+7z77rsEBQXRrl0721HkKs6dO0dERAQnTpygSpUqhIaGUq5cuet/UEREABcVubDFYQCEh4RbTiIFISEhgTfeeIP169fbjiJXkZKSwpQpUzh16hRVq1Zl8ODBlC1b1nYsEZFCJTdrrRZqH235iI+2fGQ7hhSQl19+mQEDBtCwYUPbUeQqSpQoQYMGDbjlllsYMmSISpyISB64ZkRucvBk2xGkgOzfv58ZM2awa9cu21HkGhzHoUuXLly4cAE/Pz/bcURECiXXFLmwwDDbEaSAvPLKKzzxxBOaRNYLnThxgi+//JJevXplzxWnEicikne5LnKO49wO9AcaA2WMMT0vbq8FNAfWGmPOejSlSA79/PPPzJ8/n71799qOIr+RkJDA1KlTSUxMZOXKlYSEhNiOJCJS6OWqyDmO8wzwr0s+Zy55uxSwGHgS+MAj6Txo8e7FAIQ00jePomzChAmMGDFCS3F5mWPHjhEREUFSUhJ169alS5cutiOJiBQJOS5yjuP0AiYAq4F/Ar2Af/z6vjFmr+M43wE98MIi1/2z7gCYMeY6e0phdejQIWbPns3u3bttR5FLxMfHM23aNJKTk7n99tvp27cvvr6+tmOJiBQJuRmR+ztwAHjAGJPiOE6nK+yzA/iDJ4J5WnDDYNsRJJ+9/vrrPPLII1SpUqiXBS5Sjhw5QmRkJCkpKTRq1IjevXtTvLhrbs0VEcl3ufkbNQCYZoxJucY+cUC1G4uUPxYPWGw7guSj+Ph4pk+fridVvcyePXtISUmhcePGPPTQQ/j4+NiOJCJSpOSmyPkAadfZp0oO9rmM4zgPAO9cPP7HxpgJV9nvIWAu0NoYE52bc0jR98YbbzBkyBCqVfPKf0e4VocOHahcuTL+/v4UK+aaaStFRApMborcj8BdV3vTyVrMsi2Q4yERx3F8gPeBTsBhYLPjOIuMMTt/s1854G/At7nIKy5x7NgxpkyZQmxsrO0oAhw4cIDKlStTrlw5HMehefPmtiOJiBRZufkn8lygjeM4j13l/f8PuAOYlYtjtgH2GWP2G2PSgM/Ieljit14GXgOudVn3mpxxDs44LZxeFE2cOJGBAwdSo0YN21Fcb+/evURGRhIREUFycrLtOCIiRV5uRuQmAv2A9x3H6QP4AjiOMxa4F+gAbAUm5eKYNYFDl7w+DNx56Q6O47QCbjXGfOE4zqhcHFtcICEhgY8//pht27bZjuJ6P/zwA3PmzCEzM5M6depQsmRJ25FERIq8HBc5Y0yi4zjtgQ/Jmnrk1+Gtly7+PB8YfnFkzSMcxykGvAkMzcG+YUAYQO3atX/3vqYdKZreeust+vbtS61atWxHcbUdO3Ywb948MjMzufPOO+nSpQtZd1uIiEh+ytU8AMaYE0Bvx3FqknW/XGXgF2CjMebnPJz/CHDrJa9rXdz2q3KAP7Dq4jeFW4BFjuN0/+0DD8aYcCAcICgoSK3NBU6dOsXkyZOJiYmxHcXVtm/fzvz58zHG0LZtWzp27KgSJyJSQPI0oZMx5gjwuQfOvxlo4DhOXbIKXH/g4UvO8wtZT8IC4DjOKuBpPbUqAO+88w69evWiTp06tqO4Vnx8PPPmzQPgD3/4Ax06dFCJExEpQFZn5jTGpDuO8ySwlKzpRz4xxuxwHGc8EG2MWeSpc4XMzFqaS/PJFQ1nzpzh/fff59tv9SCzTbfccgtt27bFz8+P9u3b244jIuI6uVmi690c7mqMMX/L6XGNMUuAJb/Z9tJV9u2Q0+P+VtSeqLx+VLzQe++9R3BwMPXr17cdxZUuXLiAr68vjuPoUqqIiEW5GZF78jrvG7IegDBkzfnmVRb199jgnlh29uxZ3n33XdatW2c7iitt2LCBmJgYhgwZkj1XnIiI2JGbItfsKtsrAq2B54CVwL9uNFR+CGkUYjuCeMikSZPo0qULDRs2tB3FddauXcvy5cuBrIl/mzW72l8LIiJSEHIz/ciOa7y9znGcRcD3QBRwrX1F8uz8+fO89dZbrFq1ynYU1/nmm2+y/9xDQkJU4kREvIDHFj80xuwHFgIjPXVMTwqPCSc8Jtx2DLlBH374IR06dKBx48a2o7iGMYYVK1awatUqHMehZ8+etGrVynYsERHB80+txgMPeviYHjEiagQAYYFhlpNIXiUlJTFx4kS++uor21FcwxjDsmXLWL9+PY7j8OCDD+Lv7287loiIXOSxIudk3fH8B+Ccp47pScNbDbcdQW5QeHg4bdu21SW9AlasWDGKFStG7969NRIqIuJlHGNytgjCxTVPr6Q4Wasz/BnoAkw1xjzimXh5ExQUZKKjNWdwUZKSkkL9+vX54osvCAgIsB3HVYwxJCQkULVqVdtRRESKJMdxYowxQXn5bG5G5KLJmlrkqjku7qOF7cXj/u///o+goCCVuAKQmZnJypUrad26NeXLl8dxHJU4EREvlZsi9yZXLnKZwGlgE7DS5HSIr4DFnYsDoEa5GpaTSG6lpqYyYcIE5s+fbztKkZeZmcmCBQvYvn07+/btIywsTPPEiYh4sdxMP/J0fgbJbzXfrAmAGeOVPVOuYePGjdxyyy0EBeVp1FlyKCMjg/nz57Njxw78/Px44IEHVOJERLxcbpfo2mWM+SAf8+Sb6mWr244geTRr1izuvfde2zGKtIyMDObOncsPP/xAiRIlGDhwILfeeqvtWCIich25ubQ6Angrv4Lkt7iRcbYjSB7s2rWLOXPm8MMPP9iOUmSlp6cze/Zs9u7dS8mSJRk0aBA1a9a0HUtERHIgN0XuIFA5v4KIXMkzzzzDc889R+XK+tLLLzt37mTv3r2UKlWKwYMHU726Rq9FRAqL3BS5WUCo4zjljDFeOVecFC0rVqxgx44dzJ0713aUIq1Zs2b88ssvNGzYkGrVqtmOIyIiuZCbJbr+BewBvnYcp4PjOGXyKVO+CAwPJDA80HYMyaHMzExGjhzJhAkTKFGihO04RU5qaipnz54FwHEc7r33XpU4EZFCKDcjcsfJKn6lgeUAjuMk8fspSYwxpoJn4nnOlvgttiNILkybNo2SJUvSp08f21GKnJSUFKZPn05iYiJDhw6lfPnytiOJiEge5abI7eHaEwJ7tejhWumhsEhKSuLFF19k1qxZmv7Cw5KTk5k2bRrx8fFUqFCBjIwM25FEROQG5GYeuUI9iVdgDV1WLSzefPNN7r77btq2bWs7SpGSmJjItGnTOHbsGDfddBOhoaFUrFjRdiwREbkB1yxyjuOEAluNMdsKKI+43NGjR3nrrbfYvHmz7ShFyvnz54mIiCAhIYHKlSsTGhqqS6oiIkXA9R52mAL0LIAc+W7sqrGMXTXWdgy5jjFjxjB06FDq1atnO0qRkZaWxtSpU0lISKBKlSoMGTJEJU5EpIjIzT1yhdq4b8YBMLbDWLtB5KpiY2OZP38+u3fvth2lSPHz86NJkyb88MMPhIaGUqZMoXrgXERErsE1RW5M+zG2I8h1PPPMM7zwwgvcdNNNtqMUOR06dOCee+7Bz8/PdhQREfEg1xQ5jcR5t6+//pq9e/eyYMEC21GKhFOnTrFo0SJ69epFhQoVcBxHJU5EpAjKSZGr6DhO7dwc1BhzMI95xIUyMjIYOXIkr732msqGB5w4cYKIiAjOnTvHihUr6NWrl+1IIiKST3JS5P528UdOmRwet0DFxMUAmobEG02dOpUKFSqocHjA8ePHiYiIIDExkdtuu41u3brZjiQiIvkoJ4XrLHAmv4Pkt6CPsqbBM2MK7ZzGRdL58+cZPXo08+fP1+S/N+jo0aNMmzaNpKQk6tWrR//+/fH19bUdS0RE8lFOitxbxpjx+Z4kn7Wq3sp2BLmCiRMn0r59e9q0aWM7SqEWFxfHtGnTSElJ4fbbb6dfv34UL+51A+MiIuJhrvmbPiYsxnYE+Y24uDjeffddYmL03+ZGHThwgJSUFBo1akTv3r1V4kREXEJ/24s1o0eP5tFHH6VOnTq2oxR6bdu2pXz58jRu3BgfHx/bcUREpICoyIkV27ZtIyoqSpP/3oCff/6ZChUqZK+X6u/vbzmRiIgUtOst0VVk1JhYgxoTa9iOIYAxhqeffprRo0dr0fY8+vHHH4mMjGTq1KkkJibajiMiIpZcc0TOGFNkil78+XjbEeSizz77jJ9//pkRI0bYjlIo7d27l1mzZpGRkUHdunUpXbq07UgiImKJay6tHvnHEdsRBHjvvfd49dVXiYqK0tQYefDDDz8wZ84cMjMzCQoKomvXrpq2RUTExVxT5GqU02VVmzIzM3nmmWeIiopi3bp11K1b13akQmfHjh3MmzePzMxM7rrrLjp37qwSJyLicq4pcmJPSkoKoaGhxMfHs379eipVqmQ7UqGTkJDA559/jjGGe+65h/vvv18lTkRE3FPkwhaHARAeEm45ibucPHmSHj16ULNmTb7++mtKlixpO1KhdPPNN9O+fXsyMzPp0KGDSpyIiADgGFP0lqwKCgoy0dHRl21zxmV949MSXQVn//79dO3ale7duzNhwgSKFSsyz84UmLS0NPz8/GzHEBGRfOQ4TowxJigvn3XNiNzk4Mm2I7jK5s2b6dGjBy+88AJ/+ctfbMcplDZt2sSGDRsYMmSIpmkREZErck2RCwsMsx3BNRYvXswjjzzCxx9/TI8ePWzHKZQ2bNjAV199BWQtvxUQEGA5kYiIeCPXFDkpGB988AHjx48nKiqKO++803acQmnNmjWsWLECgODgYJU4ERG5KtcUucW7FwMQ0ijEcpKiKTMzk+eff5758+ezdu1a6tevbztSoWOM4ZtvvuGbb74BoHv37rRs2dJyKhER8WauKXLdP+sO6GGH/JCamsrQoUM5ePAg69evp0qVKrYjFUorVqxg7dq1OI5Dz549ad68ue1IIiLi5VxT5IIbBtuOUCSdPn2anj17cvPNN7Ns2TJKlSplO1KhVaJECYoVK8aDDz5I06ZNbccREZFCwDXTj4jnHThwgK5du/LAAw/wn//8R9OLeMCJEyc0oiki4jI3Mv2IvvNKnsTExHDPPfcwYsQI3nzzTZW4PDDGsHz5ck6fPp29TSVORERyQ999JdeWLFnCAw88wLvvvsvf/vY323EKpczMTBYuXMjatWuZMWMGmZmZtiOJiEgh5Joi54xzsld3kLwLDw/nkUceYeHChTz00EO24xRKmZmZLFiwgO+//x5fX1+6du2qEU0REckT1zzsIDfGGMOLL77IrFmzWLNmDQ0aNLAdqVDKyMhg3rx57Ny5Ez8/Px5++GFuu+0227FERKSQck2R07QjeZeWlsYjjzzCvn37WL9+PVWrVrUdqVBKT09n7ty57N69mxIlSjBw4EBuvfVW27FERKQQc02Rk7xJT0/nT3/6E+XKlWPFihWULl3adqRCa+/evezevZuSJUsyaNAgatasaTuSiIgUcipyck379u1j165dHDp0CB8fH9txCrXGjRvTpUsXbrvtNqpXr247joiIFAGuKXIhM7OW5lo8YLHlJIXLpEmT6N+/v0pcHqWlpZGYmMhNN90EwF133WU5kYiIFCWuKXJRe6JsRyh0Dh8+zPTp09m5c6ftKIVSamoqM2bM4MyZMwwdOjS7zImIiHiKa4rcov6LbEcodP7973/z6KOPUq1aNdtRCp2UlBQiIyM5cuQI5cuX1zxxIiKSL1xT5EIahdiOUKgcOHCA2bNns3v3bttRCp3k5GSmTZtGfHw8FStWJDQ0VKNxIiKSL1xT5CR3/vWvf/H4449ryahcSkxMZNq0aRw7doybbrqJIUOGUKFCBduxRESkiHJNkQuPCQcgLDDMchLvt2/fPhYsWMDevXttRylU0tPTiYiI4Pjx41SuXJnQ0FDKly9vO5aIiBRhrlkXaETUCEZEjbAdo1B4+eWXeeqpp3Q5MJeKFy9OixYtuPnmmxk6dKhKnIiI5DvXjMgNbzXcdoRC4YcffmDJkiXs27fPdpRCwxiD42St49u2bVtat26Nr6+v5VQiIuIGrily4SHhtiMUCuPHj+fvf/+77uvKodOnTzN//nx69uxJpUqVAFTiRESkwLjm0qpcX2xsLMuXL+epp56yHaVQOHnyJFOmTOHQoUMsX77cdhwREXEh14zIxZ2LA6BGuRqWk3ivcePG8fTTT1OuXDnbUbxeQkICERERnD9/ntq1a9O9e3fbkURExIVcU+Rqvpm1QLkZYywn8U5bt25l7dq1TJ061XYUr3f8+HEiIiJITEykTp06DBgwAD8/P9uxRETEhVxT5KqX1SLl1zJ27FieffZZSpcubTuKVzt69CgREREkJydTr149+vfvr3viRETEGtcUubiRcbYjeK3o6Giio6OZOXOm7She79ChQyQnJ9OgQQP69u1L8eKu+V9IRES8kL4LCWPGjOH555+nVKlStqN4vdatW1O2bFkaNmyIj4+P7TgiIuJyKnIut2HDBrZv3868efNsR/Fazkat9AAAIABJREFUBw8epEyZMlSuXBmAxo0bW04kIiKSxTXTjwSGBxIYHmg7htcZM2YML774IiVKlLAdxSv99NNPREZGMnXqVM6dO2c7joiIyGVcMyK3JX6L7QheZ82aNezbt49hw4bZjuKV9u3bx6xZs0hPT6dp06aUKVPGdiQREZHLuKbIRQ+Pth3B67z00kuMHj1aT11ewZ49e5g9ezYZGRm0atWK4ODg7GW4REREvIVrilxgDV1WvdQnn3zCsWPHGDx4sO0oXmfXrl3MnTuXzMxMWrduzZ/+9CeVOBER8UquKXLyPxs2bOC5555j9erVmj7jN06fPp1d4u666y46d+6sEiciIl7LNd/Fx64am/Vzh7FWc9h25MgRevfuzSeffMIdd9xhO47Xuemmm+jUqRPnz5/n/vvvV4kTERGv5hhT9JasCgoKMtHRl98T54zL+obs5iW6UlJS+MMf/kCvXr14/vnnbcfxKqmpqXpyV0RErHAcJ8YYE5SXz7pmRG5M+zG2I1hljCEsLIx69erx3HPP2Y7jVTZv3szq1asZOnRo9lxxIiIihYFripzbL6m+/fbbbN++nbVr1+py4SU2btzI0qVLgaw541TkRESkMHFNkXOzr7/+mtdff52NGzdqLrRLrFu3jmXLlgHwpz/9iaCgPI1qi4iIWOOaIhcTFwO4bxqSffv2MWjQIGbPns1tt91mO47XWL16NStXrgQgODiYwEB3fV2IiEjR4JoiF/RR1miLmx52OHfuHD169GDs2LG0b9/edhyvsWrVKr755hsAunfvTsuWLS0nEhERyRvXFLlW1VvZjlCgMjMzGTx4MO3ateOxxx6zHcerlC5dGsdx6NmzJ82bN7cdR0REJM9cU+RiwmJsRyhQ48aN48SJE8yePVsPN/xGmzZtuP3226lUqZLtKCIiIjekmO0A4nnz5s1jypQpfP755/j5+dmOY50xhuXLl5OQkJC9TSVORESKAhW5Imb79u2MGDGCefPmUa1aNdtxrDPGsHjxYtauXcuMGTPIyMiwHUlERMRjXHNptcbEGgDEjYyznCT/nDx5kp49e/LOO+/oKUyy7hNctGgR33//PcWLFyc4OBgfHx/bsURERDzGNUUu/ny87Qj5Kj09nb59+9K7d28efvhh23Gsy8zMZP78+cTGxuLr68uAAQOoW7eu7VgiIiIe5Zoid+QfR2xHyFdPP/00fn5+vPLKK7ajWJeRkcG8efPYuXMnfn5+DBw4kNq1a9uOJSIi4nGuKXI1ytWwHSHfTJ06lS+//JJvv/1Wlw7JWmpr586dlChRgkGDBlGrVi3bkURERPKFa4pcUXXixAmefvppVq5cScWKFW3H8Qq33347wcHBVK9enRo1im6BFxERcU2RC1scBkB4SLjlJJ41btw4+vfvj7+/v+0oVl24cIGzZ89mL3qvhz1ERMQNXFPkPtryEVC0itwPP/zAZ599xq5du2xHsSotLY2ZM2eSkJDA0KFDqVKliu1IIiIiBcI1RW5y8GTbETxu1KhRPPfcc64uLqmpqUyfPp1Dhw5Rrlw523FEREQKlGuKXFhgmO0IHrV8+XJ27tzJ3LlzbUexJjk5menTp3PkyBHKly/PkCFDtGKDiIi4imuKXFGSkZHByJEjee211yhRooTtOFYkJSUxbdo0jh49SsWKFRkyZIge9hAREddxTZFbvHsxACGNQiwnuXFTp06lbNmyPPTQQ7ajWJGRkUFERATHjh2jUqVKhIaGUqFCBduxRERECpxrilz3z7oDYMYYy0luzPnz5xk9ejTz58/HcRzbcazw8fEhKCiIb7/9ltDQUN0bJyIiruWaIhfcMNh2BI9444036NChA23atLEdpcAZY7LLa1BQEAEBARQv7povYRERkd9xzXfBxQMW245www4fPsx///tfvvvuO9tRCtyZM2eYO3cu3bt3p2rVqgAqcSIi4nrFbAeQnHvhhRd47LHHXLdu6OnTp5kyZQpHjhxh2bJltuOIiIh4DQ1pFBLR0dF89dVX7Nmzx3aUAnXy5EmmTp3KuXPnqFWrFg8++KDtSCIiIl7DNUXOGZd1b1VhfNjBGMPIkSMZP368q27sT0hIICIigvPnz1O7dm0efvhh1063IiIiciXWL606jvOA4zi7HcfZ5zjOc1d4/x+O4+x0HGeb4zjLHce5zUZOmxYuXMipU6d45JFHbEcpMMeOHWPKlCmcP3+eOnXqMHDgQJU4ERGR37A6Iuc4jg/wPtAJOAxsdhxnkTFm5yW7fQcEGWOSHMd5HHgd6JfbcxXGkTjIWkd01KhRvP/++/j4+NiOU2COHj1KUlIS9evXp1+/fvj6+tqOJCIi4nVsX1ptA+wzxuwHcBznM6AHkF3kjDErL9l/IzCoQBNaNmnSJBo0aEDnzp1tRylQLVq0oFSpUtSrV09Pp4qIiFyF7e+QNYFDl7w+DNx5jf3/DHyZr4m8yKlTp3jllVdYtWqV7SgF4tChQ/j6+nLLLbcA0LBhQ8uJREREvJvtIpdjjuMMAoKA9ld5PwwIA644PUfIzKyluQrTfHLjx4+nd+/eNGnSxHaUfPfzzz8zffp0fH19GT58uNZNFRERyQHbRe4IcOslr2td3HYZx3E6Ai8A7Y0xqVc6kDEmHAgHCAoK+t0NcVF7ojyRt8Ds2bOHyMhIdu7cef2dC7n9+/czc+ZM0tPTady4MeXLl7cdSUREpFCwXeQ2Aw0cx6lLVoHrDzx86Q6O47QEJgMPGGOO5/VEi/ovupGcBe7ZZ59l1KhR2asYFFX79u1j1qxZpKenExAQQEhICMWKWX+YWkREpFCwWuSMMemO4zwJLAV8gE+MMTscxxkPRBtjFgFvAGWBORfX2TxojOme23OFNArxYPL8tWrVKrZu3crMmTNtR8lXu3fvZs6cOWRkZBAYGEi3bt2y11IVERGR67M9IocxZgmw5DfbXrrk1x0LPJRFmZmZjBw5kgkTJlCyZEnbcfLN2bNns0tcmzZteOCBB1TiREREcsl6kSso4THhAIQFhllOcm2RkZH4+fnRt29f21HyVfny5enatSsnT56kY8eOKnEiIiJ54BhTOCfKvZagoCATHR192bbCsERXYmIid9xxB7Nnz+buu++2HSdfpKSkFOmRRhERkdxyHCfGGBOUl8+6ZkRueKvhtiNc18SJE7nnnnuKbInbsmULy5cvJzQ0lGrVqtmOIyIiUui5psiFh4TbjnBN/fv3Z+nSpWzZssV2lHyxefNmlizJuhXywIEDKnIiIiIe4Joi581OnjzJkiVLOHLkCOXKlbMdx+M2btzI0qVLAejcuTN33nmtxTtEREQkp1xT5OLOxQFQo1wNy0l+b9GiRXTq1KlIlrh169axbNkyALp27Urr1q0tJxIRESk6XFPkar5ZE/DOhx3mzp3LoEGDbMfwuNWrV7Ny5UoAQkJCaNWqleVEIiIiRYtrilz1stVtR7iiM2fOsGbNmiI5+W/58uUpVqwYISEhBAQE2I4jIiJS5LimyMWNjLMd4YoWL17MfffdVyTXFw0ICOC2227jpptush1FRESkSNKilpbNnTuX3r17247hEcYYVqxYQXx8fPY2lTgREZH8oyJn0dmzZ1m5ciUhIYVnHdirMcawZMmS7MvEFy5csB1JRESkyHPNpdXA8EAAYsJiLCf5ny+++IJ7772XihUr2o5yQzIzM4mKiuK7777Dx8eHkJAQfH19bccSEREp8lxT5LbEe99Eu59//jkPPfSQ7Rg3JDMzk4ULF7Jt2zaKFy/OgAEDqFevnu1YIiIiruCaIhc9PPr6OxWgxMREvv76ayZPnmw7Sp5lZGSwYMECYmNj8fX15eGHH6ZOnTq2Y4mIiLiGa4pcYI1A2xEu8+WXX3LnnXdSuXJl21Hy7NChQ8TGxuLn58fAgQOpXbu27UgiIiKu4poi522KwtOqderUoWfPnlSpUoWaNWvajiMiIuI6rnlqdeyqsYxdNdZ2jGxLliyhR48etmPk2oULFzh+/Hj26xYtWqjEiYiIWOKaIjfum3GM+2ac7RgA/PjjjxQvXpybb77ZdpRcSUtLY+bMmXz66aeXzRUnIiIidrjm0uqY9mNsR8j2n//8hyeeeIJixQpPj05NTWXGjBkcPHiQMmXKULy4a750REREvJZrvhuP7TDWdgQAjh07xqxZs/jhhx9sR8mxlJQUpk+fzuHDhylXrhyhoaFUqVLFdiwRERHXc02R8xbvvPMO/fv3p2rVqraj5EhycjKRkZHExcVRoUIFQkNDqVSpku1YIiIigouKXExc1ooONqchOXv2LOHh4WzatMlahtzIzMzMLnEVK1ZkyJAhhX4VChERkaKk8NykdYOCPgoi6KMgqxnCw8Pp1KlToVn5oFixYtlz3Q0bNkwlTkRExMu4ZkSuVfVWVs+fmprKW2+9RVRUlNUcOWGMwXEcAJo3b06TJk30cIOIiIgXcs1355iwGKvnj4yMxN/fn5YtW1rNcT1nz55l1qxZdOvWjRo1agCoxImIiHgp11xatSkzM5M33niDZ5991naUazpz5gyffvopcXFxLFu2zHYcERERuQ4NtRSAhQsXUr58ee677z7bUa7q1KlTRERE8Msvv1CjRg369OljO5KIiIhch2uKXI2JWZcJ40bGFeh5jTG89tprPPvss9n3nXmbEydOEBERwblz56hVqxYDBw6kZMmStmOJiIjIdbimyMWft7Ok1OrVqzl16hQ9e/a0cv7rSUhIYOrUqSQmJnLbbbcxYMAASpQoYTuWiIiI5IBrityRfxyxct4JEyYwatQofHx8rJz/ehISEkhKSqJu3br0798fPz8/25FEREQkh1xT5GqUq1Hg59y6dSvff/898+fPL/Bz51STJk0YOHAgtWvXxtfX13YcERERyQXXFLmCdurUKfr378/48eO97n6zw4cPA1CrVi0A6tevbzOOiIiI5JFrph8JWxxG2OKwAjlXamoqvXr1Ijg4mEcffbRAzplTBw8eZNq0aURGRnLixAnbcUREROQGuKbIfbTlIz7a8lG+nyczM5OhQ4dStWpVXn/99Xw/X24cOHCAyMhI0tLSaNCgAZUqVbIdSURERG6Aay6tTg6eXCDn+ec//8nBgwdZvnw5xYp5T0/ev38/M2fOJD09nRYtWtC9e3evyiciIiK555oiFxaY/5dVP/jgA+bNm8f69eu96r64vXv3MmvWLDIyMmjZsiUhISFeO6ediIiI5Jxrilx+i4qKYvz48axdu5YqVarYjpMtMTGROXPmkJGRQVBQEF27dlWJExERKSJcU+QW714MQEijEI8f+9SpUwwZMoQvvvjC654ALVOmDN27d+fIkSN07txZJU5ERKQIcU2R6/5ZdwDMGOPxY8fGxnLrrbdy1113efzYeZWcnEypUqUA8Pf3x9/f33IiERER8TTXFLnghsH5duwPPviAoUOH5tvxc2vr1q0sXbqUQYMGUbNmTdtxREREJJ+4psgtHrA4X44bFxfH0qVL+fDDD/Pl+LkVExNDVFQUkDXdiIqciIhI0eWaIpdfPvzwQwYMGECFChVsR2HTpk18+eWXAHTs2JF77rnHciIRERHJTypyNyA1NZXw8HBWrlxpOwobNmzgq6++AqBLly5edb+eiIiI5A/XFDlnXNbTmp582GH27Nk0a9aMxo0be+yYebFu3TqWLVsGQNeuXWndurXVPCIiIlIwNLV/HhljePfdd/nrX/9qOwo33XQTxYoVIyQkRCVORETERVwzIufpaUe+/fZbTp48SdeuXT163Lxo0qQJNWrUoGLFirajiIiISAHSiFwevffeezz55JP4+PgU+LmNMaxcuZJDhw5lb1OJExERcR8VuTyIj49nyZIlPPLIIwV+bmMMS5cuZfXq1cycOZPU1NQCzyAiIiLewTWXVkNmZi3N5Yn55CZPnkz//v0LfBTMGMMXX3xBTEwMPj4+9OjRgxIlShRoBhEREfEerilyUXuiPHKctLQ0Jk+enP2UaEHJzMxk8eLFbN26leLFi9OvXz9uv/32As0gIiIi3sU1RW5R/0UeOc6cOXNo0qQJTZs29cjxciIzM5OFCxeybds2ihcvzoABA6hXr16BnV9ERES8k2uKXEijEI8c57333uP555/3yLFyKi4ujtjYWHx9fRk4cCC33XZbgZ5fRKSounDhAocPHyYlJcV2FHGBkiVLUqtWLXx9fT12TNcUOU/YtGkTx44dIzg4uEDPW6tWLR566CHKlSvHrbfeWqDnFhEpyg4fPky5cuWoU6cOjuPYjiNFmDGGkydPcvjwYerWreux47rmqdXwmHDCY8Jv6Bjvvfcef/nLXwpkypH09HSOHj2a/bpJkyYqcSIiHpaSkkLlypVV4iTfOY5D5cqVPT7665oiNyJqBCOiRuT583FxcURFRRXIlCMXLlzgs88+49NPP71srjgREfE8lTgpKPnxteaaIje81XCGtxqep88eOnSI+++/n6effppKlSp5ONnl0tLSmDFjBj/++CPFixfHz88vX88nIiJ2+fj4EBAQgL+/PyEhIZw5cyb7vR07dvDHP/6RRo0a0aBBA15++WWM+d9KRV9++SVBQUE0adKEli1bMnLkSBu/hWv67rvv+POf/3zZtp49e3LXXXddtm3o0KHMnTv3sm1ly5bN/vWePXvo2rUrDRo0oFWrVvTt25djx47dULZTp07RqVMnGjRoQKdOnTh9+vQV9zt48CCdO3emcePGNGnShAMHDmRnrlu3LgEBAQQEBLB161YAoqKieOmll24oW065psiFh4QTHpL7S6u7du2iXbt2PProo7zwwgv5kOx/UlNTmT59OgcOHKBs2bIMHTqUatWq5es5RUTErlKlSrF161ZiY2OpVKkS77//PgDJycl0796d5557jt27d/P999+zfv16Jk2aBEBsbCxPPvkkkZGR7Ny5k+joaI9PS5Wenn7Dx3jllVcuW5f8zJkzxMTE8Msvv7B///4cHSMlJYVu3brx+OOPs3fvXrZs2cITTzxBQkLCDWWbMGEC999/P3v37uX+++9nwoQJV9wvNDSUUaNGsWvXLjZt2kTVqlWz33vjjTfYunUrW7duJSAgAIBu3bqxePFikpKSbihfTrimyOXFpk2buO+++3j55Zfz/V85KSkpREZGcvDgQcqVK8fQoUO5+eab8/WcIiLiXe6++26OHDkCwIwZM7jnnnvo3LkzAKVLl+a///1vdtl4/fXXeeGFF7jjjjuArJG9xx9//HfHPH/+PMOGDaNZs2Y0b96czz//HLh8tGvu3LkMHToUyBpleuyxx7jzzjt55plnqFOnzmWjhA0aNODYsWMkJCTw0EMP0bp1a1q3bs26det+d+5z586xbds2WrRokb1t3rx5hISE0L9/fz777LMc/bnMmDGDu+++m5CQ/81A0aFDB/z9/XP0+atZuHAhQ4YMAWDIkCEsWLDgd/vs3LmT9PR0OnXqBGT9uZUuXfqax3Uchw4dOhAV5Zk5bK/FNUUu7lwcceficrz/V199Rbdu3fj4448JDQ3Nx2RZT7LMmDGDw4cPU6FCBYYNG0blypXz9ZwiIuJdMjIyWL58Od27dweyLqsGBgZetk/9+vU5f/48Z8+eJTY29nfvX8nLL79MhQoV2L59O9u2beOPf/zjdT9z+PBh1q9fz5tvvkmPHj2YP38+AN9++y233XYb1apV429/+xt///vf2bx5M59//jmPPvro744THR39u7I1c+ZMBgwYwIABA5g5c+Z1swA5/r2eO3cu+zLnb3/s3Lnzd/sfO3aM6tWrA3DLLbdc8VLtnj17qFixIg8++CAtW7Zk1KhRZGRkZL//wgsv0Lx5c/7+979ftmxmUFAQa9asydHv70a4ZvqRmm/WBMCMMdfZE2bNmsVf//pX5s+fT7t27fI7Go7jcPfdd5OUlMSgQYMKfOkvERHJkh83o196T9uVJCcnExAQwJEjR2jcuHH2yI+nLFu27LKRr5tuuum6n+nTp0/2DA39+vVj/PjxDBs2jM8++4x+/fplH/fScnT27FnOnz9/2UhffHz8ZVeXjh07xt69e2nXrh2O4+Dr60tsbCz+/v5X/LPP7X+PcuXKZd+nlluO41zxfOnp6axZs4bvvvuO2rVr069fP6ZMmcKf//xnXn31VW655RbS0tIICwvjtddey743rmrVqsTF5XwAKa9cMyJXvWx1qpetft393n//fUaOHMmyZcvyvcRd+j9348aNefzxx1XiREQsMsZ4/Mf1/HqP3M8//4wxJvseuSZNmhATE3PZvvv376ds2bKUL1+epk2b/u793Li0tPx2SowyZcpk//ruu+9m3759JCQksGDBAh588EEga9WhjRs3Zt8fduTIkctK3K+/t0uPPXv2bE6fPk3dunWpU6cOBw4cyB6Vq1y58mUPG5w6dYoqVaoA5Pj3mtsRuWrVqhEfHw9klc5L7337Va1atQgICKBevXoUL16cnj17smXLFgCqV6+O4ziUKFGCYcOGsWnTpsv+TEuVKnXdzDfKNUUubmQccSOv3YynTp3KO++8w9q1a2nWrFm+5jl37hwfffQRP//8c/a2gpifTkREvFPp0qV59913mThxIunp6QwcOJC1a9dmr+2dnJzMX//6V5555hkARo0axSuvvMKePXuArGL14Ycf/u64nTp1yi6HQHZZqlatGrt27SIzMzP70umVOI5Dr169+Mc//kHjxo2zb/3p3Lkz7733XvZ+VxoJa9y4Mfv27ct+PXPmTP7f//t/HDhwgAMHDhATE5M9WtihQwdmzZpFWloaAFOmTOG+++4D4OGHH2b9+vV88cUX2cdavXo1sbGxl53v1xG5K/1o0qTJ7/J1796dqVOnAlkdoEePHr/bp3Xr1pw5cyb7wYoVK1ZkH+vXEmiMYcGCBZddRt6zZ88N38OXE64pcteTlpbGSy+9REREBHXq1MnXc/3yyy9MmTKF+Ph4VqxYkaN/sYmISNHXsmVLmjdvzsyZMylVqhQLFy7kX//6F40aNaJZs2a0bt2aJ598EoDmzZvz9ttvM2DAABo3boy/v/8VnwJ98cUXOX36NP7+/rRo0YKVK1cCWU9sBgcH07Zt2+z7xK6mX79+REZGZl9WBXj33XeJjo6mefPmNGnS5Iol8o477uCXX37h3LlzHDhwgJ9//vmyaUfq1q1LhQoV+PbbbwkODubee+8lMDCQgIAA1q1bx2uvvQZkjexFRUXx3nvv0aBBA5o0acKkSZNu+KHA5557jq+//poGDRqwbNkynnvuOSDr3r5f7/nz8fHhP//5D/fffz/NmjXDGMPw4VnTmQ0cOJBmzZrRrFkzTpw4wYsvvph97JUrV9KtW7cbypcTTlEsEUFBQSY6OjpXn/n444+ZM2cOS5cuzadUWc6cOcPUqVM5c+YM1atXZ9CgQdd9+kVERPLHrl27aNy4se0YRdpbb71FuXLlrvgwRFF17NgxHn74YZYvX/679670Nec4TowxJigv53LNiFxgeCCB4Vd+4uXChQu88sorjB49Ol8znDp1ik8//ZQzZ85Qs2ZNQkNDVeJERKRIe/zxxylRooTtGAXq4MGDTJw4sUDO5ZqnVrfEb7nqezNmzKBOnTr5+nDDiRMniIiI4Ny5c9x6660MHDjQdV/YIiLiPiVLlmTw4MG2YxSo1q1bF9i5XFPkoodf+VJreno6//73vwkPz/2qD7lx+vRpEhMTqVOnDgMGDNDSWyIiInLDXFPkAmtc+bLqrFmzuOWWW2jfvn2+nr9BgwYMHjyYmjVr4uvrm6/nEhEREXdwTZG7kszMTP71r3/x7rvv5sskkHFxcaSlpWU/BZvfT8OKiIiIu7jmYYexq8YydtXYy7Z9+eWXlC5dmo4dO3r8fIcPHyYiIoIZM2ZccckPERERkRvlmiI37ptxjPtm3GXb3n77bf7+9797fDTu559/Ztq0aaSmpnL77bdnz0wtIiLyW0ePHqV///7Ur1+fwMBAunbtSnh4OMHBwbajSSHgmkurY9qPuex1bGwsO3bsoG/fvh49z08//cTMmTO5cOEC/v7+9OrVi2LFXNOXRUQkF4wx9OrViyFDhmSvcPD999+zaNEiy8mksHBNwxjbYSxjO4zNfv3OO+/w+OOPe/Tp0R9//JEZM2Zw4cIFWrRooRInIiLXtHLlSnx9fXnssceyt7Vo0YJ7772X8+fP07t3b+644w4GDhyYvQrQ+PHjad26Nf7+/oSFhWVv79ChA88++yxt2rShYcOGrFmzBoCMjAyefvpp/P39ad68efayWjExMbRv357AwEC6dOmSvdyUFC6uGZG7VEJCAnPnzmX37t0eO2ZycjJz5swhPT2dli1bEhISki8PUIiISP4ZN27cVd8LDg4mMDBrBoSYmBiioqKuuu+YMWOu+t6lYmNjs4/5W9999x07duygRo0a3HPPPaxbt4527drx5JNP8tJLLwEwePBgoqKiCAkJAbKm1Nq0aRNLlixh3LhxLFu2jPDwcA4cOMDWrVspXrw4p06d4sKFCzz11FMsXLiQm2++mVmzZvHCCy/wySef5Ci3eA/XFLmYuBggaxqS8PBwHnzwQapWreqx45cqVYpevXrx008/0aVLF5U4ERG5IW3atKFWrVoABAQEcODAAdq1a8fKlSt5/fXXSUpK4tSpUzRt2jS7yD344IMABAYGcuDAAQCWLVvGY489RvHiWd/yK1WqRGxsLLGxsXTq1AnIGrW73nqr4p1cU+SCPspawiztn2lMmjSJL7/80iPHTUpKyl5mq1GjRjRq1MgjxxURkYKX05G0wMDAq46k5UbTpk2ZO3fuFd+7dPUfHx8f0tPTSUlJ4YknniA6Oppbb72VsWPHkpKS8rvP/Lr/1RhjaNq0KRs2bLjh34PY5ZobuFpVb0Wr6q2YP38+DRo0oHnz5jd8zO3bt/NKfpLlAAAXjklEQVTOO+9k/6tHREQkN/74xz+Smpp62epC27Zty76/7bd+LW1VqlTh/PnzVy2Bl+rUqROTJ0/OLnanTp2iUaNGJCQkZBe5CxcusGPHjhv97YgFrilyMWExxITF8N///pcnn3zyho+3detW5s2bR1paGgcPHvRAQhERcRvHcZg/fz7Lli2jfv36NG3alOeff55bbrnlivtXrFiR4cOH4+/vT5cuXXK0puejjz5K7dq1ad68OS1atGDGjBn4+fkxd+5cnn32WVq0aEFAQADr16/39G9PCoDz69MuRUlQUJCJjv792qrff/893bp146effrqhZbIuvcn1vvvu4w9/+EOejyUiIvbs2rWLxo0b244hLnKlrznHcWKMMUF5OZ5r7pEDeP/993nsscduqMRt2rQp+/66Tp060bZtW0/FExEREckV1xS56v+pzrEKx4gfnvd5cjZu3MjSpUsBeOCBB7jzzjs9FU9EREQk11xT5I4mHoWyUK1atTwfo0qVKhQvXpwuXboQFJSnEVARERERj3FNkbtr3V08+uijN3SM22+/naeeeory5ct7KJWIiIhI3rniqdWffvqJfd/tY3DPwbn6nDGGlStXsn///uxtKnEiIiLiLVxR5CIjI+nXr1+u1lU1xvDVV1+xevVqZs+eTXJycj4mFBEREcm9Il/kjDFERERwuNVhwhaH5fgzX375JRs3bqRYsWL06NGDUqVK5XNSERFxIx8fHwICAvD396dPnz4kJSXl6vNr1qyhadOmBAQE5HrQYcGCBezcuTNXn8mLKVOmXHcO15zsI79X5Ivcd999B8DCQwv5aMtH193fGENUVBSbN2/Gx8eHfv36aY4hERHJN6VKlWLr1q3Exsbi5+fHhx9+mOPPZmRkMH36dJ5//nm2bt2a60GHgipykn+KfJH7+uuveeCBB5gcPJnJwZOvuW9mZiYLFy5ky5YtFC9enAEDBtCwYcMCSioiIm537733sm/fPiDrtqA2bdoQEBDAiBEjyMjIAKBs2bKMHDmSFi1a8OqrrzJ79mxGjx7NwIEDAXjjjTdo3bo1zZs3v2zt2IiIiOzVHQYPHsz69etZtGgRo0aNIiAggB9//PGyLEOHDuXxxx/nrrvuol69eqxatYpHHnmExo0bM3To0Oz9Zs6cSbNmzfD39+fZZ5/N3v7pp5/SsGFD2rRpw7p167K3JyQk8NBDD9G6dWtat2592XuSB8aYIvcjMDDQ/Kpjx45m4cKFJifi4+PNyy+/bP7973+b/fv35+gzIiJSeO3cufOy14zFMJbLtgXPCDaMxSz6YVH2tsnRkw1jMcMXDc/eduTsEcNYTPX/VM9VhjJlyhhjjLlw4YLp3r27mTRpktm5c6cJDg42aWlpxhhjHn/8cTN16tSsjGBmzZqV/fkhQ4aYOXPmGGOMWbp0qRk+fLjJzMw0GRkZplu3buabb74xsbGxpkGDBiYhIcEYY8zJkyd/99nfGjJkiOnXr5/JzMw0CxYsMOXKlTPb/v/27j26yurM4/j3x6Vk5CZKRQRERbyAgAiKSB1ELl5GQZdaFTQ4Uukase3U0uk40pGqXdKytK1otdgqMloo2GpxahURBbHFKUq9X6CVS8ByichFISThmT/2Tjw5nCTvCZDDSZ7PWmclZ7/77Pc5705ynux3v/t9800rLy+30047zZYvX27r1q2zLl262MaNG620tNSGDBliTz75pK1fv76yvKSkxM466yybMGGCmZldffXV9vLLL5uZ2erVq+2kk04yM7NHHnmksk5Dlv4zZ2YGLLM65jwNevmRnTt3snTp0kQ3FQY48sgjKy+K6Nq16wGOzjnnnAufVaeeeioQRuTGjRvH9OnTee211yrvpbpz506OOOIIIMypu+yyyzK2NX/+fObPn0/fvn0B2LFjBytWrOCNN97giiuuoH379gAcdthhiWK7+OKLkUSvXr3o0KEDvXr1AqBnz56sWrWK1atXc8455/DlL38ZgDFjxrB48WKAKuVXXnklH374IQALFiyocjp327Zt7NixI+HRcukadCK3ZMkSevfuTdu2bXn6g6cBuPjEi6vUKSsrY8OGDXTq1AmA7t2713uczjnnDg522973H3/66qf3Khvfbzzj+1W9gO6o1kdlfH1tKubIVYnDjLFjx3LXXXftVb+goICmTZtmbMvMuOWWW/j6179epXzatGlZxwXQokULAJo0aVL5fcXzsrKyOt3ycs+ePSxdupSCgoI6xeSqatBz5BYsWMDw4cMBGDl7JCNnj6yyvaysjDlz5jBjxgw++uijXITonHPO7WXo0KE88cQTbNy4EYBPPvmE1atX1/q68847j4cffrhyhGvdunVs3LiRc889l7lz51JcXFzZHkDr1q3Zvn17neM844wzWLRoEZs3b6a8vJxZs2YxePBgBgwYwKJFiyguLqa0tJS5c+dWvmbEiBFVEsv0JNZlp0Encs8//zzDhg0D4KITLuKiEy6q3FZaWsqsWbNYsWIFzZs39+VFnHPOHTR69OjBnXfeyYgRI+jduzfDhw/n449rv1f4iBEjGD16NAMHDqRXr15cfvnlbN++nZ49e3LrrbcyePBg+vTpw8033wzAVVddxdSpU+nbt+9eFzsk0bFjR6ZMmcKQIUPo06cP/fr1Y9SoUXTs2JHJkyczcOBABg0aVGX1h3vvvZdly5bRu3dvevTokdVVum5vCnPsGpb+/fvbs88+S7du3di8efNeQ7+7d+9m1qxZrFq1ipYtW1JYWFg598A551zj8d577/kSU65eZfqZk/SamdXpJu4Ndo7cnDlzGD58+F5JXElJCY8//jhr166lVatWjB07tnLyp3POOedcPmmQiZyZMWXKlCrn5CvKZ8+ezdq1a2nTpg2FhYUcfvjhOYrSOeecc27fNMhEbvPmzfTo0YMBAwZUlukHAmDlNSvZvn07Y8aMoV27drkK0TnnnHNunzXIRG7r1q3ccMMNlc9T5wF269aNG2+8kSZNGvR1Hs455xIyMyTlOgzXCByI6xIaZDZjZpVXoe7YsYOHHnqIldesrFzfx5M455xzENZkKy4uPiAfsM6lMjOKi4v3+/p5DXJErsK2bduYOXMmxcXFLFy4kOOOO87/63LOOVepc+fOFBUVsWnTplyH4hqBgoICOnfuvF/bzHkiJ+l84GdAU+CXZjYlbXsLYCbQDygGrjSzVbW1W1JSwowZM9iyZQsdOnRg9OjRnsQ555yronnz5hx77LG5DsO5OsvpOUZJTYH7gQuAHsDVknqkVRsHbDGz44GfAD+qrd0mTZrw/vvvs2XLFjp27EhhYSEtW7bc3+E755xzzuVUrieLnQGsNLO/m9luYDYwKq3OKODR+P0TwFDVMrRWUFDA7t276dSpE4WFhRxyyCH7PXDnnHPOuVzLdSLXCVib8rwolmWsY2ZlwFagxsXfJNGqVSuuvfZavymvc8455xqsnM+R218kjQfGx6clEydOfHvixIm5DMnVXXtgc66DcHXifZffvP/yl/ddfjuxri/MdSK3DuiS8rxzLMtUp0hSM6At4aKHKsxsOjAdQNKyut6zzOWe91/+8r7Lb95/+cv7Lr9JWlbX1+b61OpfgO6SjpX0JeAqYF5anXnA2Pj95cBC8wV/nHPOOedyOyJnZmWSbgKeIyw/8rCZvSPpdmCZmc0DfgX8j6SVwCeEZM8555xzrtHL9alVzOwZ4Jm0sv9O+X4XcEWWzU7fD6G53PH+y1/ed/nN+y9/ed/ltzr3n/wspXPOOedcfsr1HDnnnHPOOVdHeZ3ISTpf0geSVkr6zwzbW0j6Tdz+qqRj6j9Kl0mCvrtZ0ruS3pT0gqSuuYjTZVZb/6XUu0ySSfKr6Q4iSfpP0lfj7+A7kn5d3zG6zBL87Txa0ouSlse/nxfmIk63N0kPS9oo6e1qtkvSvbFv35R0WpJ28zaRO1C393IHXsK+Ww70N7PehDt6/Lh+o3TVSdh/SGoNfAt4tX4jdDVJ0n+SugO3AIPMrCfw7/UeqNtLwt+9ScAcM+tLuDjw5/UbpavBDOD8GrZfAHSPj/HAA0kazdtEjgN0ey9XL2rtOzN70cw+j0+XEtYYdAeHJL97AHcQ/nnaVZ/BuVol6b8bgPvNbAuAmW2s5xhdZkn6zoA28fu2wPp6jM/VwMwWE1bfqM4oYKYFS4FDJXWsrd18TuQOyO29XL1I0nepxgF/PKARuWzU2n/xlEAXM/tDfQbmEkny+3cCcIKkVyQtlVTTKIKrP0n6bjJwjaQiwooQ36if0Nx+kO1nI3AQLD/iXE0kXQP0BwbnOhaXjKQmwD3AdTkOxdVdM8LpnXMIo+GLJfUys09zGpVL4mpghpndLWkgYR3WU8xsT64DcwdGPo/IZXN7L2q6vZerd0n6DknDgFuBkWZWUk+xudrV1n+tgVOAlyStAs4E5vkFDweNJL9/RcA8Mys1s4+ADwmJncutJH03DpgDYGZ/BgoI92F1B79En43p8jmR89t75a9a+05SX+AXhCTO5+ccXGrsPzPbambtzewYMzuGMMdxpJnV+V6Cbr9K8rfzKcJoHJLaE061/r0+g3QZJem7NcBQAEknExK5TfUapaureUBhvHr1TGCrmX1c24vy9tSq394rfyXsu6lAK2BuvD5ljZmNzFnQrlLC/nMHqYT99xwwQtK7QDnwXTPzsxk5lrDvvgM8JOnbhAsfrvMBjIODpFmEf5DaxzmMtwHNAczsQcKcxguBlcDnwL8matf71znnnHMuP+XzqVXnnHPOuUbNEznnnHPOuTzliZxzzjnnXJ7yRM4555xzLk95Iuecc845l6c8kXPO1UjSMEkmaVKuYzmY1PW4SCqKSyI559w+80TOuQYgJhQ1Pa7LdYyNhaQlkspyHUeuSGoWf+YW5DoW5xqDvF0Q2DmX0Q+qKf9rvUbROPwJOJnsV80fTFio1Tnn9pkncs41IGY2OdcxNBZm9jnwfh1e97cDEI5zrpHyU6vONTKSTpT0I0nLJG2SVCJplaRfSOqURTvdJP1S0t8k7ZRULOktSQ9Iapeh/hhJL0n6VNIuSe9K+q94z8ik+3wsnrY7WtJ3JX0Q21or6W5Jrat53emSnkx7v/dJOjJD3SMl3RPb/izG+76kRyQdk1Kvyhw5ScdLMmAQ0DTt1PaClNdVmSMnaVKsM6Ga2LtIKpe0NK28maSbJL0qabukzyW9LulGxfvaJTymSySVSWohabKkD+Mx+mXcfqik/5D0oqR1knZL2ijpKUkD0tr6GlAanw5NOwaT0uoOlPRbSRtim2slPSipY9LYnXM+IudcY3QFMB54EXiF8MHbC7gBuEhS/9pu1BwTvr8Q7of7DPAE8E/AsUAh8DNgS0r9R2P5mlh3K3AW8EPgXEnnmVl5Fu9hGnA2MCe2dQFwM/AVSf9sZiUp+74k1rO47zXA6cAEYJSkQWa2JtZtSThlegzwPOEm1k2ArsClwG+AVdXE9Anh1Pb1QGfg9pRtNd1wfmasWwjcn2H7tTGGGSnv6UvAH4BhhFHBx4AS4NzYxukkvE9jiieBUwn38XwSqPgZOAW4E1gEPA18SjgeI4ELJV1oZhWJ6uvAHcD3gY/ie6uwOCX+G4AHgZ2EY1wEnMAXP4MDzGxdlvE71ziZmT/84Y88fxCSFAMmZ3hcl1a3M9AiQxsXAHuAaWnlw2Lbk1LKKm7IPSFDO62AgpTnX4t156SWx213VNdONe/zsVh/I9Alpbwp8FTcdktKeRtCQlkGnJXW1q2x/jMpZZfGsqkZ9t0CaF3TcYnlS4CyGt5DEbAyreyF2NZJGep/AOwC2qWU3Rnr/xRomnYcZsRt/5LwmC6J9ZcDh2fYfmg15V2BfwBvpZU3i+0tqGZ/JwO74/vqmLZtBFAOzM3175Q//JEvDz+16lzDcluGx3WpFcysyFJGrFLK/0gY3Tkvi/3tzNDODjPblVL0LcIH99fSyiGMYH0KjMlinwA/MbO1KfssB75LSCCuT6l3KSER+bWZ/SmtjR8Da4ELMpxSzvS+Ssxse5ZxJvVo/Do2tVDSmYSRqnlmtiWWNSWMJq4DvmMpI5nx+4nxabbHdJKZFacXmtmn1ZSvBn4HnCLpqCz2cyPQHPimpY38mtl8wgjvJXF01DlXCz+16lwDYma1zo2K86euJSQNvYF2hJGcCp8n2NXvCaNpD0q6kHA67hXgPTOrvCIzzlk7BdgA3FzN1K1dhFGabCxKLzCzFZLWA8dLah2TrtPi5oUZ6pdKehkYTTiluI5wuvljYJKk0wlJxSvAX81sT5YxZuO3hFOi10i6NWVfFYndjJS6JxOS0w3A9/fjMf2/6jZIOhv4JnAmcASQPq+xE7A+4X4Gxq9DJA3MsL094bPpeOCNhG0612h5Iudc43MvcBPhg/dZQgJTMVJ2PVDr6IqZ/T1OdL+NMIJ3Wdy0RtJUM7svPj8sfu0Q61Yn23XXNlRT/g9CUtEG2A60jeXVzfmrKD8UwuhTHAWbDFwMnB+3b5J0P/BDM9vva8SZ2WeS5hLmtQ0FnpfUAriS8J6eS6l+ePx6IjUf01ZZhFBuZhmXUZF0BTCbMEr5PGG+32eE0/DnEuYqtshiXxXxf6+WetnE71yj5Ymcc41IvCJwAmGkY5CZfZa2/dqkbZnZO8BXJTUD+hDmN30DmCZpu5k9SrgQAeAvZnbG/ngPUQcg0zIeFVehbotft6aVp+uYVg8LFz5cL6kJ0IOQrNxESO6g+rX69tWjhERuLCFhGkkYLb3bql4IUhHrXDP76gGKJdUdhES/n5l9kLpBUhdCIpeNivhbWljCxTm3D3yOnHONSzdAwHMZkriuhKs1s2JmZWb2mpndxRfzsi6J2z4lTGrvJenQfQk8zeD0AkndCaOJK1Pmsi2PX8/JUL85YamQ1HqVzGyPmb1tZvfyxbzBSxLEVh6aT74ESLSYMNp1aTwlXXFa9dG0eu8QRhsHxiT6QOsGvJ0hiWvKF8cvVcVp4aYZtgFULKOSbQLonMvAEznnGpdV8evZ8YMYqJzLNp2EfxMk9ZfUJsOmDvFr6kjLPUAB8CtJbdNfIOkwSX2T7DfFt+NoUEUbTYGphCT1kZR6vyNcTHFNnPOW6juEKy+ftbjUhaRTJB2RYX+Z3ld1ignHsXOSN1Ihzi2cCRxCGDU9D3jdzN5Kq1cK3Bfb/6mkgvS2JB0lKds5ctVZDZyolDX3YpJ6O+H0bvr72EO4UvjoatqbRjiV/jNJx6dvlPQlSV/ZH4E71xj4qVXnGhEzK5L0BHA58HpcqLYt4bToDuAtwunE2lwHjIsXC/yNkCwdT5hXtouwjlzFPqdL6kdYu26wpPmEtdwOA44jjMw8RDh9mdSfgTckpa4j14uwtt3dKfveJmkcYf23l+M8tLVAf2A4YZ7gv6W0ez5wl6Q/AR8Sbr/VBRhFGGmamiC2FwhXyz4l6VnC3LKPzOzxBK+dSZj3djvh73P6aFyF2wgXqlSshbcwvpcOQHfCGn3fA95LsM/a/ISQOP5V0m8JSdjZhKtp/xe4KMNrXgAul/R7wmhnGfCSmS0xs3fiwsEPAe9K+iOwgjDP7ujY9nrCRTLOudrkev0Tf/jDH/v+IK4jl7BuS+AuYCUh6VpD+KBuR4Y10Mi8jtxAwoKubxIWwt0Z23sY6FHNfkcSFrHdRFiE+B/Aq4Q5WCcmjL1iHbmjCcuNfEBYCLeIMPLXuprXDSCsM7eZsBTKauDn7L2OWU9C4rIsxrmLMIo5BziztuMSy5sBUwgL4paStqYaGdaRS3v9S/E1u4H2NdRrQjj9ujD2wW7ChSsvA7cAnRMe0xrXvYt1rifMq/w8HsPfxWNVsZ7dV9LqHwnMIqz3V17NcepDSFRXxz78BHgbeAA4J9e/U/7wR748ZOb3bnbO5QdJjxHm4XUxs6Jcx+Occ7nmc+Scc8455/KUJ3LOOeecc3nKEznnnHPOuTzlc+Scc8455/KUj8g555xzzuUpT+Scc8455/KUJ3LOOeecc3nKEznnnHPOuTzliZxzzjnnXJ7yRM4555xzLk/9P5k1q89WfWE5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_roc_plot(y_predicted_proba[:,1], y_test, figsize=(10,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_confusion_matrix_plot(y_predicted, y_test, figsize=(9, 9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\tBrier: 0.234\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_calibration_plot(y_predicted_proba[:,1], y_test, figsize=(9,9))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inference Function\n",
    "\n",
    "Just like for our first model, we define an inference function that takes in an arbitrary question and outputs an estimated probability of it receiving a high score according to our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:00<00:00, 81.02it/s]\n",
      "100%|██████████| 1/1 [00:00<00:00, 978.38it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.36 probability of the question receiving a high score according to our model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "pos_prob = get_question_score_from_input(\"\"\"\n",
    "When quoting a person's informal speech, how much liberty do you have to make changes to what they say?\n",
    "\"\"\")\n",
    "\n",
    "print(\"%s probability of the question receiving a high score according to our model\" % (pos_prob))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ml_editor",
   "language": "python",
   "name": "ml_editor"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
